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<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Curiously , the spear-phishing emails were strikingly similar to the e-fax themed spam usually seen spreading ransomware and other common crimeware .' [/INST] [Curiously:O, ,:O, the:O, spear-phishing:S-ACT, emails:S-TOOL, were:O, strikingly:O, similar:O, to:O, the:O, e-fax:O, themed:O, spam:O, usually:O, seen:O, spreading:O, ransomware:O, and:O, other:O, common:O, crimeware:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Due to the sheer number of recipients , it may not have been possible to customize the emails in the same way as was possible with lower-volume campaigns .' [/INST] [Due:O, to:O, the:O, sheer:O, number:O, of:O, recipients:O, ,:O, it:O, may:O, not:O, have:O, been:O, possible:O, to:O, customize:O, the:O, emails:S-TOOL, in:O, the:O, same:O, way:O, as:O, was:O, possible:O, with:O, lower-volume:O, campaigns:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The similarity to common spam may however also serve a more devious purpose .' [/INST] [The:O, similarity:O, to:O, common:O, spam:O, may:O, however:O, also:O, serve:O, a:O, more:O, devious:O, purpose:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It is easy to imagine a security analyst , burdened by the amount of attacks against their network , dismissing such common-looking spam as β just another crimeware spam run β , allowing the campaign to , in essence , hide in the masses .' [/INST] [It:O, is:O, easy:O, to:O, imagine:O, a:O, security:O, analyst:O, ,:O, burdened:O, by:O, the:O, amount:O, of:O, attacks:O, against:O, their:O, network:O, ,:O, dismissing:O, such:O, common-looking:O, spam:O, as:O, β:O, just:O, another:O, crimeware:O, spam:O, run:O, β:O, ,:O, allowing:O, the:O, campaign:O, to:O, ,:O, in:O, essence:O, ,:O, hide:O, in:O, the:O, masses:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The CozyDuke activity continues one of the long-running trends of the Dukes operations , the use of multiple malware toolsets against a single target .' [/INST] [The:O, CozyDuke:S-MAL, activity:O, continues:O, one:O, of:O, the:O, long-running:O, trends:O, of:O, the:O, Dukes:S-APT, operations:O, ,:O, the:O, use:O, of:O, multiple:O, malware:O, toolsets:O, against:O, a:O, single:O, target:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In this case , the Dukes first attempted to infect large numbers of potential targets with CozyDuke ( and in a more obvious manner than previously seen ) .' [/INST] [In:O, this:O, case:O, ,:O, the:O, Dukes:S-APT, first:O, attempted:O, to:O, infect:O, large:O, numbers:O, of:O, potential:O, targets:O, with:O, CozyDuke:S-MAL, (:O, and:O, in:O, a:O, more:O, obvious:O, manner:O, than:O, previously:O, seen:O, ):O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'They would then use the toolset to gather initial information on the victims , before deciding which ones to pursue further .' [/INST] [They:O, would:O, then:O, use:O, the:O, toolset:O, to:O, gather:O, initial:O, information:O, on:O, the:O, victims:O, ,:O, before:O, deciding:O, which:O, ones:O, to:O, pursue:O, further:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'For the victims deemed interesting enough , the Dukes would then deploy a different toolset .' [/INST] [For:O, the:O, victims:O, deemed:O, interesting:O, enough:O, ,:O, the:O, Dukes:S-APT, would:O, then:O, deploy:O, a:O, different:O, toolset:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We believe the primary purpose of this tactic is an attempt at evading detection in the targeted network .' [/INST] [We:O, believe:O, the:O, primary:O, purpose:O, of:O, this:O, tactic:O, is:O, an:O, attempt:O, at:O, evading:O, detection:O, in:O, the:O, targeted:O, network:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Even if the noisy initial CozyDuke campaign is noticed by the victim organization , or by someone else who then makes it publicly known , defenders will begin by first looking for indicators of compromise ( IOCs ) related to the CozyDuke toolset .' [/INST] [Even:O, if:O, the:O, noisy:O, initial:O, CozyDuke:S-MAL, campaign:O, is:O, noticed:O, by:O, the:O, victim:O, organization:O, ,:O, or:O, by:O, someone:O, else:O, who:O, then:O, makes:O, it:O, publicly:O, known:O, ,:O, defenders:O, will:O, begin:O, by:O, first:O, looking:O, for:O, indicators:B-TOOL, of:I-TOOL, compromise:E-TOOL, (:O, IOCs:S-TOOL, ):O, related:O, to:O, the:O, CozyDuke:S-MAL, toolset:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'If however by that time the Dukes are already operating within the victim βs network , using an another toolset with different IOCs , then it is reasonable to assume that it will take much longer for the victim organization to notice the infiltration .' [/INST] [If:O, however:O, by:O, that:O, time:O, the:O, Dukes:S-APT, are:O, already:O, operating:O, within:O, the:O, victim:O, βs:O, network:O, ,:O, using:O, an:O, another:O, toolset:O, with:O, different:O, IOCs:S-TOOL, ,:O, then:O, it:O, is:O, reasonable:O, to:O, assume:O, that:O, it:O, will:O, take:O, much:O, longer:O, for:O, the:O, victim:O, organization:O, to:O, notice:O, the:O, infiltration:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In previous cases , the group used their malware toolsets interchangeably , as either the initial or a later-stage toolset in a campaign .' [/INST] [In:O, previous:O, cases:O, ,:O, the:O, group:O, used:O, their:O, malware:O, toolsets:O, interchangeably:O, ,:O, as:O, either:O, the:O, initial:O, or:O, a:O, later-stage:O, toolset:O, in:O, a:O, campaign:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'For these CozyDuke campaigns however , the Dukes appear to have employed two particular later-stage toolsets , SeaDuke and HammerDuke , that were purposely designed to leave a persistent backdoor on the compromised network .' [/INST] [For:O, these:O, CozyDuke:S-MAL, campaigns:O, however:O, ,:O, the:O, Dukes:S-APT, appear:O, to:O, have:O, employed:O, two:O, particular:O, later-stage:O, toolsets:O, ,:O, SeaDuke:S-MAL, and:O, HammerDuke:S-MAL, ,:O, that:O, were:O, purposely:O, designed:O, to:O, leave:O, a:O, persistent:O, backdoor:O, on:O, the:O, compromised:O, network:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'HammerDuke is a set of backdoors that was first seen in the wild in February 2015 , while SeaDuke is a crossplatform backdoor that was , according to Symantec , first spotted in the wild in October 2014 .' [/INST] [HammerDuke:S-MAL, is:O, a:O, set:O, of:O, backdoors:O, that:O, was:O, first:O, seen:O, in:O, the:O, wild:O, in:O, February:B-TIME, 2015:E-TIME, ,:O, while:O, SeaDuke:S-MAL, is:O, a:O, crossplatform:O, backdoor:O, that:O, was:O, ,:O, according:O, to:O, Symantec:S-SECTEAM, ,:O, first:O, spotted:O, in:O, the:O, wild:O, in:O, October:B-TIME, 2014:E-TIME, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Both toolsets were originally spotted being deployed by CozyDuke to its victims .' [/INST] [Both:O, toolsets:O, were:O, originally:O, spotted:O, being:O, deployed:O, by:O, CozyDuke:S-MAL, to:O, its:O, victims:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'What makes SeaDuke special is that it was written in Python and designed to work on both Windows and Linux systems ; it is the first cross-platform tool we have seen from the Dukes .' [/INST] [What:O, makes:O, SeaDuke:S-MAL, special:O, is:O, that:O, it:O, was:O, written:O, in:O, Python:S-TOOL, and:O, designed:O, to:O, work:O, on:O, both:O, Windows:S-OS, and:O, Linux:S-OS, systems:O, ;:O, it:O, is:O, the:O, first:O, cross-platform:O, tool:O, we:O, have:O, seen:O, from:O, the:O, Dukes:S-APT, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'One plausible reason for developing such a flexible malware might be that the group were increasingly encountering victim environments where users were using Linux as their desktop operating system .' [/INST] [One:O, plausible:O, reason:O, for:O, developing:O, such:O, a:O, flexible:O, malware:O, might:O, be:O, that:O, the:O, group:O, were:O, increasingly:O, encountering:O, victim:O, environments:O, where:O, users:O, were:O, using:O, Linux:S-OS, as:O, their:O, desktop:O, operating:O, system:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Meanwhile , HammerDuke is a Windows only malware ( written in .NET ) and comes in two variants .' [/INST] [Meanwhile:O, ,:O, HammerDuke:S-MAL, is:O, a:O, Windows:S-OS, only:O, malware:O, (:O, written:O, in:O, .NET:S-TOOL, ):O, and:O, comes:O, in:O, two:O, variants:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The simpler one will connect to a hardcoded C&C server over HTTP or HTTPS to download commands to execute .' [/INST] [The:O, simpler:O, one:O, will:O, connect:O, to:O, a:O, hardcoded:O, C&C:S-TOOL, server:O, over:O, HTTP:S-PROT, or:O, HTTPS:S-PROT, to:O, download:O, commands:O, to:O, execute:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The more advanced variant , on the other hand , will use an algorithm to generate a periodically-changing Twitter account name and will then attempt to find tweets from that account containing links to the actual download location of the commands to execute .' [/INST] [The:O, more:O, advanced:O, variant:O, ,:O, on:O, the:O, other:O, hand:O, ,:O, will:O, use:O, an:O, algorithm:O, to:O, generate:O, a:O, periodically-changing:O, Twitter:S-TOOL, account:O, name:O, and:O, will:O, then:O, attempt:O, to:O, find:O, tweets:O, from:O, that:O, account:O, containing:O, links:O, to:O, the:O, actual:O, download:O, location:O, of:O, the:O, commands:O, to:O, execute:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In this way , the advanced HammerDuke variant attempts to hide its network traffic in more legitimate use of Twitter .' [/INST] [In:O, this:O, way:O, ,:O, the:O, advanced:O, HammerDuke:S-MAL, variant:O, attempts:O, to:O, hide:O, its:O, network:O, traffic:O, in:O, more:O, legitimate:O, use:O, of:O, Twitter:S-TOOL, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This method is not unique to HammerDuke , as MiniDuke , OnionDuke , and CozyDuke all support similar use of Twitter ( image 9 , page 18 ) to retrieve links to additional payloads or commands . 2015 : CloudDuke .' [/INST] [This:O, method:O, is:O, not:O, unique:O, to:O, HammerDuke:S-MAL, ,:O, as:O, MiniDuke:S-MAL, ,:O, OnionDuke:S-MAL, ,:O, and:O, CozyDuke:S-MAL, all:O, support:O, similar:O, use:O, of:O, Twitter:S-TOOL, (:O, image:O, 9:O, ,:O, page:O, 18:O, ):O, to:O, retrieve:O, links:O, to:O, additional:O, payloads:O, or:O, commands:O, .:O, 2015:S-TIME, ::O, CloudDuke:S-MAL, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In the beginning of July 2015 , the Dukes embarked on yet another large-scale phishing campaign .' [/INST] [In:O, the:O, beginning:B-TIME, of:I-TIME, July:I-TIME, 2015:E-TIME, ,:O, the:O, Dukes:S-APT, embarked:O, on:O, yet:O, another:O, large-scale:O, phishing:S-ACT, campaign:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The malware toolset used for this campaign was the previously unseen CloudDuke and we believe that the July campaign marks the first time that this toolset was deployed by the Dukes , other than possible small-scale testing .' [/INST] [The:O, malware:O, toolset:O, used:O, for:O, this:O, campaign:O, was:O, the:O, previously:O, unseen:O, CloudDuke:S-MAL, and:O, we:O, believe:O, that:O, the:O, July:S-TIME, campaign:O, marks:O, the:O, first:O, time:O, that:O, this:O, toolset:O, was:O, deployed:O, by:O, the:O, Dukes:S-APT, ,:O, other:O, than:O, possible:O, small-scale:O, testing:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The CloudDuke toolset consists of at least a loader , a downloader , and two backdoor variants .' [/INST] [The:O, CloudDuke:S-MAL, toolset:O, consists:O, of:O, at:O, least:O, a:O, loader:S-MAL, ,:O, a:O, downloader:S-MAL, ,:O, and:O, two:O, backdoor:O, variants:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Both backdoors ( internally referred to by their authors as β BastionSolution β and β OneDriveSolution β ) essentially allow the operator to remotely execute commands on the compromised machine .' [/INST] [Both:O, backdoors:O, (:O, internally:O, referred:O, to:O, by:O, their:O, authors:O, as:O, β:O, BastionSolution:S-MAL, β:O, and:O, β:O, OneDriveSolution:S-MAL, β:O, ):O, essentially:O, allow:O, the:O, operator:O, to:O, remotely:O, execute:O, commands:O, on:O, the:O, compromised:O, machine:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The way in which each backdoor does so however is significantly different .' [/INST] [The:O, way:O, in:O, which:O, each:O, backdoor:O, does:O, so:O, however:O, is:O, significantly:O, different:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'While the BastionSolution variant simply retrieves commands from a hard-coded C&C server controlled by the Dukes , the OneDriveSolution utilizes Microsoft βs OneDrive cloud storage service for communicating with its masters , making it significantly harder for defenders to notice the traffic and block the communication channel .' [/INST] [While:O, the:O, BastionSolution:S-MAL, variant:O, simply:O, retrieves:O, commands:O, from:O, a:O, hard-coded:O, C&C:S-TOOL, server:O, controlled:O, by:O, the:O, Dukes:S-APT, ,:O, the:O, OneDriveSolution:S-MAL, utilizes:O, Microsoft:S-IDTY, βs:O, OneDrive:S-TOOL, cloud:O, storage:O, service:O, for:O, communicating:O, with:O, its:O, masters:O, ,:O, making:O, it:O, significantly:O, harder:O, for:O, defenders:O, to:O, notice:O, the:O, traffic:O, and:O, block:O, the:O, communication:O, channel:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'What is most significant about the July 2015 CloudDuke campaign is the timeline .' [/INST] [What:O, is:O, most:O, significant:O, about:O, the:O, July:B-TIME, 2015:E-TIME, CloudDuke:S-MAL, campaign:O, is:O, the:O, timeline:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The campaign appeared to consist of two distinct waves of spear-phishing , one during the first days of July and the other starting from the 20th of the month .' [/INST] [The:O, campaign:O, appeared:O, to:O, consist:O, of:O, two:O, distinct:O, waves:O, of:O, spear-phishing:S-ACT, ,:O, one:O, during:O, the:O, first:B-TIME, days:I-TIME, of:I-TIME, July:E-TIME, and:O, the:O, other:O, starting:O, from:O, the:O, 20th:B-TIME, of:I-TIME, the:I-TIME, month:E-TIME, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Details of the first wave , including a thorough technical analysis of CloudDuke , was published by Palo Alto Networks on 14th July .' [/INST] [Details:O, of:O, the:O, first:O, wave:O, ,:O, including:O, a:O, thorough:O, technical:O, analysis:O, of:O, CloudDuke:S-MAL, ,:O, was:O, published:O, by:O, Palo:B-SECTEAM, Alto:I-SECTEAM, Networks:E-SECTEAM, on:O, 14th:B-TIME, July:E-TIME, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This was followed by additional details from Kaspersky in a blog post published on 16th July .' [/INST] [This:O, was:O, followed:O, by:O, additional:O, details:O, from:O, Kaspersky:S-SECTEAM, in:O, a:O, blog:O, post:O, published:O, on:O, 16th:B-TIME, July:E-TIME, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Both publications happened before the second wave took place and received notable publicity .' [/INST] [Both:O, publications:O, happened:O, before:O, the:O, second:O, wave:O, took:O, place:O, and:O, received:O, notable:O, publicity:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Despite the attention and public exposure of the toolset βs technical details ( including IOCs ) to defenders , the Dukes still continued with their second wave of spear-phishing , including the continued use of CloudDuke .' [/INST] [Despite:O, the:O, attention:O, and:O, public:O, exposure:O, of:O, the:O, toolset:O, βs:O, technical:O, details:O, (:O, including:O, IOCs:S-TOOL, ):O, to:O, defenders:O, ,:O, the:O, Dukes:S-APT, still:O, continued:O, with:O, their:O, second:O, wave:O, of:O, spear-phishing:S-ACT, ,:O, including:O, the:O, continued:O, use:O, of:O, CloudDuke:S-MAL, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The group did change the contents of the spear-phishing emails they sent , but they didnβt switch to a new email format ; instead , they reverted to the same efaxthemed format that they had previously employed , even to the point of reusing the exact same decoy document that they had used in the CozyDuke campaign a year earlier ( July 2014 ) .' [/INST] [The:O, group:O, did:O, change:O, the:O, contents:O, of:O, the:O, spear-phishing:S-ACT, emails:S-TOOL, they:O, sent:O, ,:O, but:O, they:O, didnβt:O, switch:O, to:O, a:O, new:O, email:S-TOOL, format:O, ;:O, instead:O, ,:O, they:O, reverted:O, to:O, the:O, same:O, efaxthemed:O, format:O, that:O, they:O, had:O, previously:O, employed:O, ,:O, even:O, to:O, the:O, point:O, of:O, reusing:O, the:O, exact:O, same:O, decoy:O, document:O, that:O, they:O, had:O, used:O, in:O, the:O, CozyDuke:S-MAL, campaign:O, a:O, year:O, earlier:O, (:O, July:B-TIME, 2014:E-TIME, ):O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This once more highlights two crucial behavioral elements of the Dukes group .' [/INST] [This:O, once:O, more:O, highlights:O, two:O, crucial:O, behavioral:O, elements:O, of:O, the:O, Dukes:S-APT, group:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Firstly , as with the MiniDuke campaigns of February 2013 and CosmicDuke campaigns in the summer of 2014 , again the group clearly prioritized the continuation of their operations over maintaining stealth .' [/INST] [Firstly:O, ,:O, as:O, with:O, the:O, MiniDuke:S-MAL, campaigns:O, of:O, February:B-TIME, 2013:E-TIME, and:O, CosmicDuke:S-MAL, campaigns:O, in:O, the:O, summer:B-TIME, of:I-TIME, 2014:E-TIME, ,:O, again:O, the:O, group:O, clearly:O, prioritized:O, the:O, continuation:O, of:O, their:O, operations:O, over:O, maintaining:O, stealth:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Secondly , it underlines their boldness , arrogance and self-confidence ; they are clearly confident in both their ability to compromise their targets even when their tools and techniques are already publicly known , and critically , they appear to be extremely confident in their ability to act with impunity . 2015 : Continuing surgical strikes with CosmicDuke .' [/INST] [Secondly:O, ,:O, it:O, underlines:O, their:O, boldness:O, ,:O, arrogance:O, and:O, self-confidence:O, ;:O, they:O, are:O, clearly:O, confident:O, in:O, both:O, their:O, ability:O, to:O, compromise:O, their:O, targets:O, even:O, when:O, their:O, tools:O, and:O, techniques:O, are:O, already:O, publicly:O, known:O, ,:O, and:O, critically:O, ,:O, they:O, appear:O, to:O, be:O, extremely:O, confident:O, in:O, their:O, ability:O, to:O, act:O, with:O, impunity:O, .:O, 2015:S-TIME, ::O, Continuing:O, surgical:O, strikes:O, with:O, CosmicDuke:S-MAL, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In addition to the notably overt and large-scale campaigns with CozyDuke and CloudDuke , the Dukes also continued to engage in more covert , surgical campaigns using CosmicDuke .' [/INST] [In:O, addition:O, to:O, the:O, notably:O, overt:O, and:O, large-scale:O, campaigns:O, with:O, CozyDuke:S-MAL, and:O, CloudDuke:S-MAL, ,:O, the:O, Dukes:S-APT, also:O, continued:O, to:O, engage:O, in:O, more:O, covert:O, ,:O, surgical:O, campaigns:O, using:O, CosmicDuke:S-MAL, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The latest of these campaigns that we are aware of occurred during the spring and early summer of 2015 .' [/INST] [The:O, latest:O, of:O, these:O, campaigns:O, that:O, we:O, are:O, aware:O, of:O, occurred:O, during:O, the:O, spring:S-TIME, and:O, early:B-TIME, summer:I-TIME, of:I-TIME, 2015:E-TIME, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'As their infection vectors , these campaigns used malicious documents exploiting recently fixed vulnerabilities .' [/INST] [As:O, their:O, infection:O, vectors:O, ,:O, these:O, campaigns:O, used:O, malicious:O, documents:O, exploiting:O, recently:O, fixed:O, vulnerabilities:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Two of these campaigns were detailed in separate blog posts by the Polish security company Prevenity , who said that both campaigns targeted Polish entities with spear- phishing emails containing malicious attachments with relevant Polish language names .' [/INST] [Two:O, of:O, these:O, campaigns:O, were:O, detailed:O, in:O, separate:O, blog:O, posts:O, by:O, the:O, Polish:O, security:O, company:O, Prevenity:S-SECTEAM, ,:O, who:O, said:O, that:O, both:O, campaigns:O, targeted:O, Polish:O, entities:O, with:O, spear-:B-ACT, phishing:E-ACT, emails:S-TOOL, containing:O, malicious:O, attachments:O, with:O, relevant:O, Polish:O, language:O, names:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'A third , similar , CosmicDuke campaign was observed presumably targeting Georgian entities since it used an attachment with a Georgian-language name that translates to β NATO consolidates control of the Black Sea.docx β .' [/INST] [A:O, third:O, ,:O, similar:O, ,:O, CosmicDuke:S-MAL, campaign:O, was:O, observed:O, presumably:O, targeting:O, Georgian:S-LOC, entities:O, since:O, it:O, used:O, an:O, attachment:O, with:O, a:O, Georgian-language:O, name:O, that:O, translates:O, to:O, β:O, NATO:B-FILE, consolidates:I-FILE, control:I-FILE, of:I-FILE, the:I-FILE, Black:I-FILE, Sea.docx:E-FILE, β:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Based on this , we do not believe that the Dukes are replacing their covert and targeted campaigns with the overt and opportunistic CozyDuke and CloudDuke style of campaigns .' [/INST] [Based:O, on:O, this:O, ,:O, we:O, do:O, not:O, believe:O, that:O, the:O, Dukes:S-APT, are:O, replacing:O, their:O, covert:O, and:O, targeted:O, campaigns:O, with:O, the:O, overt:O, and:O, opportunistic:O, CozyDuke:S-MAL, and:O, CloudDuke:S-MAL, style:O, of:O, campaigns:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Instead , we believe that they are simply expanding their activities by adding new tools and techniques .' [/INST] [Instead:O, ,:O, we:O, believe:O, that:O, they:O, are:O, simply:O, expanding:O, their:O, activities:O, by:O, adding:O, new:O, tools:O, and:O, techniques:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'A XENOTIME to Remember : Veles in the Wild .' [/INST] [A:O, XENOTIME:S-APT, to:O, Remember:O, ::O, Veles:S-TOOL, in:O, the:O, Wild:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'FireEye recently published a blog covering the tactics , techniques , and procedures ( TTPs ) for the β TRITON actor β when preparing to deploy the TRITON / TRISIS malware framework in 2017 .' [/INST] [FireEye:S-SECTEAM, recently:O, published:O, a:O, blog:O, covering:O, the:O, tactics:O, ,:O, techniques:O, ,:O, and:O, procedures:O, (:O, TTPs:O, ):O, for:O, the:O, β:O, TRITON:S-MAL, actor:O, β:O, when:O, preparing:O, to:O, deploy:O, the:O, TRITON:S-MAL, /:O, TRISIS:S-MAL, malware:O, framework:O, in:O, 2017:S-TIME, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Overall , the post does a commendable job in making public findings previously only privately shared ( presumably by FireEye , and in several reports I authored for my employer , Dragos ) to threat intelligence customers .' [/INST] [Overall:O, ,:O, the:O, post:O, does:O, a:O, commendable:O, job:O, in:O, making:O, public:O, findings:O, previously:O, only:O, privately:O, shared:O, (:O, presumably:O, by:O, FireEye:S-SECTEAM, ,:O, and:O, in:O, several:O, reports:O, I:O, authored:O, for:O, my:O, employer:O, ,:O, Dragos:S-SECTEAM, ):O, to:O, threat:O, intelligence:O, customers:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'As such , the blog continues to push forward the narrative of how ICS attacks are enabled through prepositioning and initial intrusion operations β an item I have discussed at length .' [/INST] [As:O, such:O, ,:O, the:O, blog:O, continues:O, to:O, push:O, forward:O, the:O, narrative:O, of:O, how:O, ICS:S-TOOL, attacks:O, are:O, enabled:O, through:O, prepositioning:O, and:O, initial:O, intrusion:O, operations:O, β:O, an:O, item:O, I:O, have:O, discussed:O, at:O, length:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Yet one point of confusion in the blog comes at the very start : referring to the entity responsible for TRITON as the β TRITON actor β .' [/INST] [Yet:O, one:O, point:O, of:O, confusion:O, in:O, the:O, blog:O, comes:O, at:O, the:O, very:O, start:O, ::O, referring:O, to:O, the:O, entity:O, responsible:O, for:O, TRITON:S-MAL, as:O, the:O, β:O, TRITON:S-MAL, actor:O, β:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This seems confusing as FireEye earlier publicly declared the β TRITON actor β as a discrete entity , linked to a Russian research institution , and christened it as β TEMP.Veles β .' [/INST] [This:O, seems:O, confusing:O, as:O, FireEye:S-SECTEAM, earlier:O, publicly:O, declared:O, the:O, β:O, TRITON:S-MAL, actor:O, β:O, as:O, a:O, discrete:O, entity:O, ,:O, linked:O, to:O, a:O, Russian:O, research:O, institution:O, ,:O, and:O, christened:O, it:O, as:O, β:O, TEMP.Veles:S-APT, β:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In the 2018 public posting announcing TEMP.Veles , FireEye researchers noted that the institute in question at least supported TEMP.Veles activity in deploying TRITON , with subsequent public presentations at Cyberwarcon and the Kaspersky Lab sponsored Security Analyst Summit essentially linking TRITON and the research institute ( and therefore TEMP.Veles ) as one in the same .' [/INST] [In:O, the:O, 2018:S-TIME, public:O, posting:O, announcing:O, TEMP.Veles:S-APT, ,:O, FireEye:S-SECTEAM, researchers:O, noted:O, that:O, the:O, institute:O, in:O, question:O, at:O, least:O, supported:O, TEMP.Veles:S-APT, activity:O, in:O, deploying:O, TRITON:S-MAL, ,:O, with:O, subsequent:O, public:O, presentations:O, at:O, Cyberwarcon:S-SECTEAM, and:O, the:O, Kaspersky:B-SECTEAM, Lab:E-SECTEAM, sponsored:O, Security:B-SECTEAM, Analyst:I-SECTEAM, Summit:E-SECTEAM, essentially:O, linking:O, TRITON:S-MAL, and:O, the:O, research:O, institute:O, (:O, and:O, therefore:O, TEMP.Veles:S-MAL, ):O, as:O, one:O, in:O, the:O, same:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Yet the most-recent posting covering TTPs from initial access through prerequisites to enable final delivery of effects on target ( deploying TRITON / TRISIS ) avoids the use of the TEMP.Veles term entirely .' [/INST] [Yet:O, the:O, most-recent:O, posting:O, covering:O, TTPs:O, from:O, initial:O, access:O, through:O, prerequisites:O, to:O, enable:O, final:O, delivery:O, of:O, effects:O, on:O, target:O, (:O, deploying:O, TRITON:S-MAL, /:O, TRISIS:S-MAL, ):O, avoids:O, the:O, use:O, of:O, the:O, TEMP.Veles:S-APT, term:O, entirely:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In subsequent discussion , FireEye personnel indicate that there was not β an avalanche of evidence to substantiate β anything more than β TRITON actor β β summing matters by indicating this term β is the best we βve got for the public for now β .' [/INST] [In:O, subsequent:O, discussion:O, ,:O, FireEye:S-SECTEAM, personnel:O, indicate:O, that:O, there:O, was:O, not:O, β:O, an:O, avalanche:O, of:O, evidence:O, to:O, substantiate:O, β:O, anything:O, more:O, than:O, β:O, TRITON:S-MAL, actor:O, β:O, β:O, summing:O, matters:O, by:O, indicating:O, this:O, term:O, β:O, is:O, the:O, best:O, we:O, βve:O, got:O, for:O, the:O, public:O, for:O, now:O, β:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Meanwhile , parallel work at Dragos ( my employer , where I have performed significant work on the activity described above ) uncovered similar conclusions concerning TTPs and behaviors , for both the 2017 event and subsequent activity in other industrial sectors .' [/INST] [Meanwhile:O, ,:O, parallel:O, work:O, at:O, Dragos:S-SECTEAM, (:O, my:O, employer:O, ,:O, where:O, I:O, have:O, performed:O, significant:O, work:O, on:O, the:O, activity:O, described:O, above:O, ):O, uncovered:O, similar:O, conclusions:O, concerning:O, TTPs:O, and:O, behaviors:O, ,:O, for:O, both:O, the:O, 2017:S-TIME, event:O, and:O, subsequent:O, activity:O, in:O, other:O, industrial:O, sectors:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Utilizing Diamond Model methodology for characterizing activity by behaviors attached to victims , we began tracking TRITON / TRISIS and immediate enabling activity as a distinct activity group ( collection of behaviors , infrastructure , and victimology ) designated XENOTIME .' [/INST] [Utilizing:O, Diamond:B-TOOL, Model:E-TOOL, methodology:O, for:O, characterizing:O, activity:O, by:O, behaviors:O, attached:O, to:O, victims:O, ,:O, we:O, began:O, tracking:O, TRITON:S-MAL, /:O, TRISIS:S-MAL, and:O, immediate:O, enabling:O, activity:O, as:O, a:O, distinct:O, activity:O, group:O, (:O, collection:O, of:O, behaviors:O, ,:O, infrastructure:O, ,:O, and:O, victimology:O, ):O, designated:O, XENOTIME:S-APT, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Based on information gained from discussion with the initial TRITON / TRISIS responders and subsequent work on follow-on activity by this entity , Dragos developed a comprehensive ( public ) picture of adversary activity roughly matching FireEye βs analysis published in April 2019 , described in various media .' [/INST] [Based:O, on:O, information:O, gained:O, from:O, discussion:O, with:O, the:O, initial:O, TRITON:S-MAL, /:O, TRISIS:S-MAL, responders:O, and:O, subsequent:O, work:O, on:O, follow-on:O, activity:O, by:O, this:O, entity:O, ,:O, Dragos:S-SECTEAM, developed:O, a:O, comprehensive:O, (:O, public:O, ):O, picture:O, of:O, adversary:O, activity:O, roughly:O, matching:O, FireEye:S-SECTEAM, βs:O, analysis:O, published:O, in:O, April:B-TIME, 2019:E-TIME, ,:O, described:O, in:O, various:O, media:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'At this stage , we have two similar , parallel constructions of events β the how behind the immediate deployment and execution of TRITON / TRISIS β yet dramatically different responses in terms of attribution and labeling .' [/INST] [At:O, this:O, stage:O, ,:O, we:O, have:O, two:O, similar:O, ,:O, parallel:O, constructions:O, of:O, events:O, β:O, the:O, how:O, behind:O, the:O, immediate:O, deployment:O, and:O, execution:O, of:O, TRITON:S-MAL, /:O, TRISIS:S-MAL, β:O, yet:O, dramatically:O, different:O, responses:O, in:O, terms:O, of:O, attribution:O, and:O, labeling:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Since late 2018 , based upon the most-recent posting , FireEye appears to have β walked back β the previously-used terminology of TEMP.Veles and instead refers rather cryptically to the β TRITON actor β , while Dragos leveraged identified behaviors to consistently refer to an activity group , XENOTIME .' [/INST] [Since:O, late:B-TIME, 2018:E-TIME, ,:O, based:O, upon:O, the:O, most-recent:O, posting:O, ,:O, FireEye:S-SECTEAM, appears:O, to:O, have:O, β:O, walked:O, back:O, β:O, the:O, previously-used:O, terminology:O, of:O, TEMP.Veles:S-APT, and:O, instead:O, refers:O, rather:O, cryptically:O, to:O, the:O, β:O, TRITON:S-MAL, actor:O, β:O, ,:O, while:O, Dragos:S-SECTEAM, leveraged:O, identified:O, behaviors:O, to:O, consistently:O, refer:O, to:O, an:O, activity:O, group:O, ,:O, XENOTIME:S-APT, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Given that both organizations appear to describe similar ( if not identical ) activity , any reasonable person could ( and should ) ask β why the inconsistency in naming and identification .' [/INST] [Given:O, that:O, both:O, organizations:O, appear:O, to:O, describe:O, similar:O, (:O, if:O, not:O, identical:O, ):O, activity:O, ,:O, any:O, reasonable:O, person:O, could:O, (:O, and:O, should:O, ):O, ask:O, β:O, why:O, the:O, inconsistency:O, in:O, naming:O, and:O, identification:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Aside from the competitive vendor naming landscape ( which I am not a fan of in cases on direct overlap , but which has more to say for itself when different methodologies are employed around similar observations ) , the distinction between FireEye and Dragos β approaches with respect to the β TRITON actor β comes down to fundamental philosophical differences in methodology .' [/INST] [Aside:O, from:O, the:O, competitive:O, vendor:O, naming:O, landscape:O, (:O, which:O, I:O, am:O, not:O, a:O, fan:O, of:O, in:O, cases:O, on:O, direct:O, overlap:O, ,:O, but:O, which:O, has:O, more:O, to:O, say:O, for:O, itself:O, when:O, different:O, methodologies:O, are:O, employed:O, around:O, similar:O, observations:O, ):O, ,:O, the:O, distinction:O, between:O, FireEye:S-SECTEAM, and:O, Dragos:S-SECTEAM, β:O, approaches:O, with:O, respect:O, to:O, the:O, β:O, TRITON:S-MAL, actor:O, β:O, comes:O, down:O, to:O, fundamental:O, philosophical:O, differences:O, in:O, methodology:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'As wonderfully described in a recent public posting , FireEye adheres to a naming convention based upon extensive data collection and activity comparison , designed to yield the identification of a discrete , identifiable entity responsible for a given collection of activity .' [/INST] [As:O, wonderfully:O, described:O, in:O, a:O, recent:O, public:O, posting:O, ,:O, FireEye:S-SECTEAM, adheres:O, to:O, a:O, naming:O, convention:O, based:O, upon:O, extensive:O, data:O, collection:O, and:O, activity:O, comparison:O, ,:O, designed:O, to:O, yield:O, the:O, identification:O, of:O, a:O, discrete:O, ,:O, identifiable:O, entity:O, responsible:O, for:O, a:O, given:O, collection:O, of:O, activity:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This technique is precise and praiseworthy β yet at the same time , appears so rigorous as to impose limitations on the ability to dynamically adjust and adapt to emerging adversary activity . ( Or for that matter , even categorize otherwise well-known historical actors operating to the present day , such as Turla . ) FireEye βs methodology may have particular limitations in instances where adversaries ( such as XENOTIME and presumably TEMP.Veles ) rely upon extensive use of publicly-available , commonly-used tools with limited amounts of customization .' [/INST] [This:O, technique:O, is:O, precise:O, and:O, praiseworthy:O, β:O, yet:O, at:O, the:O, same:O, time:O, ,:O, appears:O, so:O, rigorous:O, as:O, to:O, impose:O, limitations:O, on:O, the:O, ability:O, to:O, dynamically:O, adjust:O, and:O, adapt:O, to:O, emerging:O, adversary:O, activity:O, .:O, (:O, Or:O, for:O, that:O, matter:O, ,:O, even:O, categorize:O, otherwise:O, well-known:O, historical:O, actors:O, operating:O, to:O, the:O, present:O, day:O, ,:O, such:O, as:O, Turla:S-APT, .:O, ):O, FireEye:S-SECTEAM, βs:O, methodology:O, may:O, have:O, particular:O, limitations:O, in:O, instances:O, where:O, adversaries:O, (:O, such:O, as:O, XENOTIME:S-APT, and:O, presumably:O, TEMP.Veles:S-APT, ):O, rely:O, upon:O, extensive:O, use:O, of:O, publicly-available:O, ,:O, commonly-used:O, tools:O, with:O, limited:O, amounts:O, of:O, customization:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In such cases , utilizing purely technical approaches for differentiation ( an issue I lightly touched on in a recent post ) becomes problematic , especially when trying to define attribution to specific , β who-based β entities ( such as a Russian research institute ) .' [/INST] [In:O, such:O, cases:O, ,:O, utilizing:O, purely:O, technical:O, approaches:O, for:O, differentiation:O, (:O, an:O, issue:O, I:O, lightly:O, touched:O, on:O, in:O, a:O, recent:O, post:O, ):O, becomes:O, problematic:O, ,:O, especially:O, when:O, trying:O, to:O, define:O, attribution:O, to:O, specific:O, ,:O, β:O, who-based:O, β:O, entities:O, (:O, such:O, as:O, a:O, Russian:O, research:O, institute:O, ):O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'My understanding is FireEye labels entities where definitive attribution is not yet possible with the β TEMP β moniker ( hence , TEMP.Veles ) β yet in this case FireEye developed and deployed the label , then appeared to move away from it in subsequent reporting .' [/INST] [My:O, understanding:O, is:O, FireEye:S-SECTEAM, labels:O, entities:O, where:O, definitive:O, attribution:O, is:O, not:O, yet:O, possible:O, with:O, the:O, β:O, TEMP:O, β:O, moniker:O, (:O, hence:O, ,:O, TEMP.Veles:S-APT, ):O, β:O, yet:O, in:O, this:O, case:O, FireEye:S-SECTEAM, developed:O, and:O, deployed:O, the:O, label:O, ,:O, then:O, appeared:O, to:O, move:O, away:O, from:O, it:O, in:O, subsequent:O, reporting:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Based on the public blog post β which also indicated that FireEye is responding to an intrusion at a second facility featuring the same or similar observations β this is presumably not for lack of evidence , yet the β downgrade β occurs all the same .' [/INST] [Based:O, on:O, the:O, public:O, blog:O, post:O, β:O, which:O, also:O, indicated:O, that:O, FireEye:S-SECTEAM, is:O, responding:O, to:O, an:O, intrusion:O, at:O, a:O, second:O, facility:O, featuring:O, the:O, same:O, or:O, similar:O, observations:O, β:O, this:O, is:O, presumably:O, not:O, for:O, lack:O, of:O, evidence:O, ,:O, yet:O, the:O, β:O, downgrade:O, β:O, occurs:O, all:O, the:O, same:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In comparison , XENOTIME was defined based on principles of infrastructure ( compromised third-party infrastructure and various networks associated with several Russian research institutions ) , capabilities ( publicly- and commercially-available tools with varying levels of customization ) and targeting ( an issue not meant for discussion in this blog ) .' [/INST] [In:O, comparison:O, ,:O, XENOTIME:S-APT, was:O, defined:O, based:O, on:O, principles:O, of:O, infrastructure:O, (:O, compromised:O, third-party:O, infrastructure:O, and:O, various:O, networks:O, associated:O, with:O, several:O, Russian:O, research:O, institutions:O, ):O, ,:O, capabilities:O, (:O, publicly-:O, and:O, commercially-available:O, tools:O, with:O, varying:O, levels:O, of:O, customization:O, ):O, and:O, targeting:O, (:O, an:O, issue:O, not:O, meant:O, for:O, discussion:O, in:O, this:O, blog:O, ):O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In personally responding to several incidents across multiple industry sectors since early 2018 matching TTPs from the TRITON / TRISIS event , these items proved consistent and supported the creation of the XENOTIME activity group .' [/INST] [In:O, personally:O, responding:O, to:O, several:O, incidents:O, across:O, multiple:O, industry:O, sectors:O, since:O, early:B-TIME, 2018:E-TIME, matching:O, TTPs:O, from:O, the:O, TRITON:S-MAL, /:O, TRISIS:S-MAL, event:O, ,:O, these:O, items:O, proved:O, consistent:O, and:O, supported:O, the:O, creation:O, of:O, the:O, XENOTIME:S-APT, activity:O, group:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This naming decision was founded upon the underlying methodology described in the Diamond Model of intrusion analysis .' [/INST] [This:O, naming:O, decision:O, was:O, founded:O, upon:O, the:O, underlying:O, methodology:O, described:O, in:O, the:O, Diamond:B-TOOL, Model:E-TOOL, of:O, intrusion:O, analysis:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'As such , this decision does not necessarily refer to a specific institution , but rather a collection of observations and behaviors observed across multiple , similarly-situated victims .' [/INST] [As:O, such:O, ,:O, this:O, decision:O, does:O, not:O, necessarily:O, refer:O, to:O, a:O, specific:O, institution:O, ,:O, but:O, rather:O, a:O, collection:O, of:O, observations:O, and:O, behaviors:O, observed:O, across:O, multiple:O, ,:O, similarly-situated:O, victims:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Of note , this methodology of naming abstracts away the β who β element β XENOTIME may represent a single discrete entity ( such as a Russian research institution ) or several entities working in coordination in a roughly repeatable , similar manner across multiple events .' [/INST] [Of:O, note:O, ,:O, this:O, methodology:O, of:O, naming:O, abstracts:O, away:O, the:O, β:O, who:O, β:O, element:O, β:O, XENOTIME:S-APT, may:O, represent:O, a:O, single:O, discrete:O, entity:O, (:O, such:O, as:O, a:O, Russian:O, research:O, institution:O, ):O, or:O, several:O, entities:O, working:O, in:O, coordination:O, in:O, a:O, roughly:O, repeatable:O, ,:O, similar:O, manner:O, across:O, multiple:O, events:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Ultimately , the epistemic foundation of the behavior-based naming approach makes this irrelevant for tracking ( and labeling for convenience sake ) observations .' [/INST] [Ultimately:O, ,:O, the:O, epistemic:O, foundation:O, of:O, the:O, behavior-based:O, naming:O, approach:O, makes:O, this:O, irrelevant:O, for:O, tracking:O, (:O, and:O, labeling:O, for:O, convenience:O, sake:O, ):O, observations:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Much like the observers watching the shadows of objects cast upon the wall of the cave , these two definitions ( XENOTIME and TEMP.Veles , both presumably referring to β the TRITON actor β ) describe the same phenomena , yet at the same time appear different .' [/INST] [Much:O, like:O, the:O, observers:O, watching:O, the:O, shadows:O, of:O, objects:O, cast:O, upon:O, the:O, wall:O, of:O, the:O, cave:O, ,:O, these:O, two:O, definitions:O, (:O, XENOTIME:S-APT, and:O, TEMP.Veles:S-APT, ,:O, both:O, presumably:O, referring:O, to:O, β:O, the:O, TRITON:S-MAL, actor:O, β:O, ):O, describe:O, the:O, same:O, phenomena:O, ,:O, yet:O, at:O, the:O, same:O, time:O, appear:O, different:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This question of perception and accuracy rests upon the underlying epistemic framework and the goal conceived for that framework in defining an adversary : FireEye βs methodology follows a deductive approach requiring the collection of significant evidence over time to yield a conclusion that will be necessary given the premises ( the totality of evidence suggests APTxx ) ; the Dragos approach instead seeks an inductive approach , where premises may all be true but the conclusion need not necessarily follow from them given changes in premises over time or other observations not contained within the set ( thus , identified behaviors strongly suggests an activity group , defined as X ) .' [/INST] [This:O, question:O, of:O, perception:O, and:O, accuracy:O, rests:O, upon:O, the:O, underlying:O, epistemic:O, framework:O, and:O, the:O, goal:O, conceived:O, for:O, that:O, framework:O, in:O, defining:O, an:O, adversary:O, ::O, FireEye:S-SECTEAM, βs:O, methodology:O, follows:O, a:O, deductive:O, approach:O, requiring:O, the:O, collection:O, of:O, significant:O, evidence:O, over:O, time:O, to:O, yield:O, a:O, conclusion:O, that:O, will:O, be:O, necessary:O, given:O, the:O, premises:O, (:O, the:O, totality:O, of:O, evidence:O, suggests:O, APTxx:O, ):O, ;:O, the:O, Dragos:S-SECTEAM, approach:O, instead:O, seeks:O, an:O, inductive:O, approach:O, ,:O, where:O, premises:O, may:O, all:O, be:O, true:O, but:O, the:O, conclusion:O, need:O, not:O, necessarily:O, follow:O, from:O, them:O, given:O, changes:O, in:O, premises:O, over:O, time:O, or:O, other:O, observations:O, not:O, contained:O, within:O, the:O, set:O, (:O, thus:O, ,:O, identified:O, behaviors:O, strongly:O, suggests:O, an:O, activity:O, group:O, ,:O, defined:O, as:O, X:O, ):O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'From an external analysts β point of view , the wonder is , which is superior to the other .' [/INST] [From:O, an:O, external:O, analysts:O, β:O, point:O, of:O, view:O, ,:O, the:O, wonder:O, is:O, ,:O, which:O, is:O, superior:O, to:O, the:O, other:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'And my answer for this is : neither is perfect , but both are useful β depending upon your goals and objectives .' [/INST] [And:O, my:O, answer:O, for:O, this:O, is:O, ::O, neither:O, is:O, perfect:O, ,:O, but:O, both:O, are:O, useful:O, β:O, depending:O, upon:O, your:O, goals:O, and:O, objectives:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'But rather than trying to pursue some comparison between the two for identification of superiority ( an approach that will result in unproductive argument and social media warring ) , the point of this post is to highlight the distinctions between these approaches and how β in the case of β the TRITON actor β β they result in noticeably different conclusions from similar datasets .' [/INST] [But:O, rather:O, than:O, trying:O, to:O, pursue:O, some:O, comparison:O, between:O, the:O, two:O, for:O, identification:O, of:O, superiority:O, (:O, an:O, approach:O, that:O, will:O, result:O, in:O, unproductive:O, argument:O, and:O, social:O, media:O, warring:O, ):O, ,:O, the:O, point:O, of:O, this:O, post:O, is:O, to:O, highlight:O, the:O, distinctions:O, between:O, these:O, approaches:O, and:O, how:O, β:O, in:O, the:O, case:O, of:O, β:O, the:O, TRITON:S-MAL, actor:O, β:O, β:O, they:O, result:O, in:O, noticeably:O, different:O, conclusions:O, from:O, similar:O, datasets:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'One reason for the distinction may be differences in evidence , as FireEye βs public reporting notes two distinct events of which they are aware of and have responded to related to β the TRITON actor β while Dragos has been engaged several instances β thus , Dragos would possess more evidence to cement the definition of an activity group , while FireEye βs data collection-centric approach would require far more observations to yield an β APT β .' [/INST] [One:O, reason:O, for:O, the:O, distinction:O, may:O, be:O, differences:O, in:O, evidence:O, ,:O, as:O, FireEye:S-SECTEAM, βs:O, public:O, reporting:O, notes:O, two:O, distinct:O, events:O, of:O, which:O, they:O, are:O, aware:O, of:O, and:O, have:O, responded:O, to:O, related:O, to:O, β:O, the:O, TRITON:S-MAL, actor:O, β:O, while:O, Dragos:S-SECTEAM, has:O, been:O, engaged:O, several:O, instances:O, β:O, thus:O, ,:O, Dragos:S-SECTEAM, would:O, possess:O, more:O, evidence:O, to:O, cement:O, the:O, definition:O, of:O, an:O, activity:O, group:O, ,:O, while:O, FireEye:S-SECTEAM, βs:O, data:O, collection-centric:O, approach:O, would:O, require:O, far:O, more:O, observations:O, to:O, yield:O, an:O, β:O, APT:O, β:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Yet irrespective of this , it is confusing why the previously-declared β TEMP β category was walked back as this has led to not small amount of confusion β in both technical and non-technical audiences β as to just what FireEye βs blog post refers .' [/INST] [Yet:O, irrespective:O, of:O, this:O, ,:O, it:O, is:O, confusing:O, why:O, the:O, previously-declared:O, β:O, TEMP:O, β:O, category:O, was:O, walked:O, back:O, as:O, this:O, has:O, led:O, to:O, not:O, small:O, amount:O, of:O, confusion:O, β:O, in:O, both:O, technical:O, and:O, non-technical:O, audiences:O, β:O, as:O, to:O, just:O, what:O, FireEye:S-SECTEAM, βs:O, blog:O, post:O, refers:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Thus respected journalists ( at least by me ) conflate the β TRITON actor is active at another site β with β TRITON malware was identified at another site β .' [/INST] [Thus:O, respected:O, journalists:O, (:O, at:O, least:O, by:O, me:O, ):O, conflate:O, the:O, β:O, TRITON:S-MAL, actor:O, is:O, active:O, at:O, another:O, site:O, β:O, with:O, β:O, TRITON:S-MAL, malware:O, was:O, identified:O, at:O, another:O, site:O, β:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In this case , we βre seeing a definite problem with the overly-conservative naming approach used as it engenders confusion in a significant subset of the intended audience .' [/INST] [In:O, this:O, case:O, ,:O, we:O, βre:O, seeing:O, a:O, definite:O, problem:O, with:O, the:O, overly-conservative:O, naming:O, approach:O, used:O, as:O, it:O, engenders:O, confusion:O, in:O, a:O, significant:O, subset:O, of:O, the:O, intended:O, audience:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'While some may dismiss adversary or activity naming as so much marketing , having a distinct label for something allows for clearer communication and more accurate discussion .' [/INST] [While:O, some:O, may:O, dismiss:O, adversary:O, or:O, activity:O, naming:O, as:O, so:O, much:O, marketing:O, ,:O, having:O, a:O, distinct:O, label:O, for:O, something:O, allows:O, for:O, clearer:O, communication:O, and:O, more:O, accurate:O, discussion:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Furthermore , conflating adversaries with tools , since tools can be repurposed or used by other entities than those first observed deploying them , leads to further potential confusion as the β X actor β is quickly compressed in the minds of some to refer to any and all instantiations of tool β X β .' [/INST] [Furthermore:O, ,:O, conflating:O, adversaries:O, with:O, tools:O, ,:O, since:O, tools:O, can:O, be:O, repurposed:O, or:O, used:O, by:O, other:O, entities:O, than:O, those:O, first:O, observed:O, deploying:O, them:O, ,:O, leads:O, to:O, further:O, potential:O, confusion:O, as:O, the:O, β:O, X:O, actor:O, β:O, is:O, quickly:O, compressed:O, in:O, the:O, minds:O, of:O, some:O, to:O, refer:O, to:O, any:O, and:O, all:O, instantiations:O, of:O, tool:O, β:O, X:O, β:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Overall , the discussion above may appear so much splitting of hairs or determining how many angels can dance on the head of a pin β yet given the communicative impacts behind different naming and labeling conventions , this exploration seems not merely useful but necessary .' [/INST] [Overall:O, ,:O, the:O, discussion:O, above:O, may:O, appear:O, so:O, much:O, splitting:O, of:O, hairs:O, or:O, determining:O, how:O, many:O, angels:O, can:O, dance:O, on:O, the:O, head:O, of:O, a:O, pin:O, β:O, yet:O, given:O, the:O, communicative:O, impacts:O, behind:O, different:O, naming:O, and:O, labeling:O, conventions:O, ,:O, this:O, exploration:O, seems:O, not:O, merely:O, useful:O, but:O, necessary:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Understanding the β how β and β why β behind different entity classifications of similar ( or even the same ) activity allows us to move beyond the dismissive approach of β everyone has their names for marketing purposes β to a more productive mindset that grasps the fundamental methodologies that ( should ) drive these decisions .' [/INST] [Understanding:O, the:O, β:O, how:O, β:O, and:O, β:O, why:O, β:O, behind:O, different:O, entity:O, classifications:O, of:O, similar:O, (:O, or:O, even:O, the:O, same:O, ):O, activity:O, allows:O, us:O, to:O, move:O, beyond:O, the:O, dismissive:O, approach:O, of:O, β:O, everyone:O, has:O, their:O, names:O, for:O, marketing:O, purposes:O, β:O, to:O, a:O, more:O, productive:O, mindset:O, that:O, grasps:O, the:O, fundamental:O, methodologies:O, that:O, (:O, should:O, ):O, drive:O, these:O, decisions:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'TRITON Attribution : Russian Government-Owned Lab Most Likely Built Custom Intrusion Tools for TRITON Attackers .' [/INST] [TRITON:S-MAL, Attribution:O, ::O, Russian:O, Government-Owned:O, Lab:O, Most:O, Likely:O, Built:O, Custom:O, Intrusion:O, Tools:O, for:O, TRITON:S-MAL, Attackers:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In a previous blog post we detailed the TRITON intrusion that impacted industrial control systems ( ICS ) at a critical infrastructure facility .' [/INST] [In:O, a:O, previous:O, blog:O, post:O, we:O, detailed:O, the:O, TRITON:S-MAL, intrusion:O, that:O, impacted:O, industrial:B-TOOL, control:I-TOOL, systems:E-TOOL, (:O, ICS:S-TOOL, ):O, at:O, a:O, critical:O, infrastructure:O, facility:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We now track this activity set as TEMP.Veles .' [/INST] [We:O, now:O, track:O, this:O, activity:O, set:O, as:O, TEMP.Veles:S-APT, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In this blog post we provide additional information linking TEMP.Veles and their activity surrounding the TRITON intrusion to a Russian government-owned research institute .' [/INST] [In:O, this:O, blog:O, post:O, we:O, provide:O, additional:O, information:O, linking:O, TEMP.Veles:S-APT, and:O, their:O, activity:O, surrounding:O, the:O, TRITON:S-MAL, intrusion:O, to:O, a:O, Russian:O, government-owned:O, research:O, institute:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'FireEye Intelligence assesses with high confidence that intrusion activity that led to deployment of TRITON was supported by the Central Scientific Research Institute of Chemistry and Mechanics ( CNIIHM ; a.k.a. Π¦ΠΠΠΠ₯Π ) , a Russian government-owned technical research institution located in Moscow .' [/INST] [FireEye:S-SECTEAM, Intelligence:O, assesses:O, with:O, high:O, confidence:O, that:O, intrusion:O, activity:O, that:O, led:O, to:O, deployment:O, of:O, TRITON:S-MAL, was:O, supported:O, by:O, the:O, Central:B-IDTY, Scientific:I-IDTY, Research:I-IDTY, Institute:I-IDTY, of:I-IDTY, Chemistry:I-IDTY, and:I-IDTY, Mechanics:E-IDTY, (:O, CNIIHM:S-IDTY, ;:O, a.k.a.:O, Π¦ΠΠΠΠ₯Π:S-IDTY, ):O, ,:O, a:O, Russian:O, government-owned:O, technical:O, research:O, institution:O, located:O, in:O, Moscow:S-LOC, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The following factors supporting this assessment are further detailed in this post .' [/INST] [The:O, following:O, factors:O, supporting:O, this:O, assessment:O, are:O, further:O, detailed:O, in:O, this:O, post:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We present as much public information as possible to support this assessment , but withheld sensitive information that further contributes to our high confidence assessment .' [/INST] [We:O, present:O, as:O, much:O, public:O, information:O, as:O, possible:O, to:O, support:O, this:O, assessment:O, ,:O, but:O, withheld:O, sensitive:O, information:O, that:O, further:O, contributes:O, to:O, our:O, high:O, confidence:O, assessment:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'FireEye uncovered malware development activity that is very likely supporting TEMP.Veles activity .' [/INST] [FireEye:S-SECTEAM, uncovered:O, malware:O, development:O, activity:O, that:O, is:O, very:O, likely:O, supporting:O, TEMP.Veles:S-APT, activity:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This includes testing multiple versions of malicious software , some of which were used by TEMP.Veles during the TRITON intrusion .' [/INST] [This:O, includes:O, testing:O, multiple:O, versions:O, of:O, malicious:O, software:O, ,:O, some:O, of:O, which:O, were:O, used:O, by:O, TEMP.Veles:S-APT, during:O, the:O, TRITON:S-MAL, intrusion:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Investigation of this testing activity reveals multiple independent ties to Russia , CNIIHM , and a specific person in Moscow .' [/INST] [Investigation:O, of:O, this:O, testing:O, activity:O, reveals:O, multiple:O, independent:O, ties:O, to:O, Russia:S-LOC, ,:O, CNIIHM:S-IDTY, ,:O, and:O, a:O, specific:O, person:O, in:O, Moscow:S-LOC, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This person βs online activity shows significant links to CNIIHM .' [/INST] [This:O, person:O, βs:O, online:O, activity:O, shows:O, significant:O, links:O, to:O, CNIIHM:S-IDTY, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'An IP address registered to CNIIHM has been employed by TEMP.Veles for multiple purposes , including monitoring open-source coverage of TRITON , network reconnaissance , and malicious activity in support of the TRITON intrusion .' [/INST] [An:O, IP:O, address:O, registered:O, to:O, CNIIHM:S-IDTY, has:O, been:O, employed:O, by:O, TEMP.Veles:S-APT, for:O, multiple:O, purposes:O, ,:O, including:O, monitoring:O, open-source:O, coverage:O, of:O, TRITON:S-MAL, ,:O, network:O, reconnaissance:O, ,:O, and:O, malicious:O, activity:O, in:O, support:O, of:O, the:O, TRITON:S-MAL, intrusion:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Behavior patterns observed in TEMP.Veles activity are consistent with the Moscow time zone , where CNIIHM is located .' [/INST] [Behavior:O, patterns:O, observed:O, in:O, TEMP.Veles:S-APT, activity:O, are:O, consistent:O, with:O, the:O, Moscow:S-LOC, time:O, zone:O, ,:O, where:O, CNIIHM:S-IDTY, is:O, located:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We judge that CNIIHM likely possesses the necessary institutional knowledge and personnel to assist in the orchestration and development of TRITON and TEMP.Veles operations .' [/INST] [We:O, judge:O, that:O, CNIIHM:S-IDTY, likely:O, possesses:O, the:O, necessary:O, institutional:O, knowledge:O, and:O, personnel:O, to:O, assist:O, in:O, the:O, orchestration:O, and:O, development:O, of:O, TRITON:S-MAL, and:O, TEMP.Veles:S-APT, operations:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'While we cannot rule out the possibility that one or more CNIIHM employees could have conducted TEMP.Veles activity without their employer βs approval , the details shared in this post demonstrate that this explanation is less plausible than TEMP.Veles operating with the support of the institute .' [/INST] [While:O, we:O, cannot:O, rule:O, out:O, the:O, possibility:O, that:O, one:O, or:O, more:O, CNIIHM:S-IDTY, employees:O, could:O, have:O, conducted:O, TEMP.Veles:S-APT, activity:O, without:O, their:O, employer:O, βs:O, approval:O, ,:O, the:O, details:O, shared:O, in:O, this:O, post:O, demonstrate:O, that:O, this:O, explanation:O, is:O, less:O, plausible:O, than:O, TEMP.Veles:S-APT, operating:O, with:O, the:O, support:O, of:O, the:O, institute:O, .:O] </s> |