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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: 'Or thirdly , the Dukes may have invested so much into these campaigns that by the time FireEye published their alert , the Dukes felt they could not afford to halt the campaigns .' [/INST] [Or:O, thirdly:O, ,:O, the:O, Dukes:S-APT, may:O, have:O, invested:O, so:O, much:O, into:O, these:O, campaigns:O, that:O, by:O, the:O, time:O, FireEye:S-SECTEAM, published:O, their:O, alert:O, ,:O, the:O, Dukes:S-APT, felt:O, they:O, could:O, not:O, afford:O, to:O, halt:O, the: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: 'We believe all three circumstances to have coexisted at least to some extent .' [/INST] [We:O, believe:O, all:O, three:O, circumstances:O, to:O, have:O, coexisted:O, at:O, least:O, to:O, some:O, extent: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 will become evident in this report , this was not a one-off case but a recurring theme with the Dukes , in that they would rather continue with their operations as planned than retreat from operating under the spotlight .' [/INST] [As:O, will:O, become:O, evident:O, in:O, this:O, report:O, ,:O, this:O, was:O, not:O, a:O, one-off:O, case:O, but:O, a:O, recurring:O, theme:O, with:O, the:O, Dukes:S-APT, ,:O, in:O, that:O, they:O, would:O, rather:O, continue:O, with:O, their:O, operations:O, as:O, planned:O, than:O, retreat:O, from:O, operating:O, under:O, the:O, spotlight: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 originally detailed in Kaspersky ’s whitepaper , the MiniDuke campaigns from February 2013 employed spear-phishing emails with malicious PDF file attachments .' [/INST] [As:O, originally:O, detailed:O, in:O, Kaspersky:S-SECTEAM, ’s:O, whitepaper:O, ,:O, the:O, MiniDuke:S-MAL, campaigns:O, from:O, February:B-TIME, 2013:E-TIME, employed:O, spear-phishing:S-ACT, emails:S-TOOL, with:O, malicious:O, PDF:S-TOOL, file:O, attachments: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: 'These PDFs would attempt to silently infect the recipient with MiniDuke , while distracting them by displaying a decoy document .' [/INST] [These:O, PDFs:S-TOOL, would:O, attempt:O, to:O, silently:O, infect:O, the:O, recipient:O, with:O, MiniDuke:S-MAL, ,:O, while:O, distracting:O, them:O, by:O, displaying:O, a:O, decoy:O, document: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 headings of these documents included “ Ukraine ’s NATO Membership Action Plan ( MAP ) Debates ” , “ The Informal Asia-Europe Meeting ( ASEM ) Seminar on Human Rights ” , and “ Ukraine ’s Search for a Regional Foreign Policy ” .' [/INST] [The:O, headings:O, of:O, these:O, documents:O, included:O, “:O, Ukraine:S-LOC, ’s:O, NATO:S-IDTY, Membership:O, Action:O, Plan:O, (:O, MAP:O, ):O, Debates:O, ”:O, ,:O, “:O, The:O, Informal:O, Asia-Europe:O, Meeting:O, (:O, ASEM:O, ):O, Seminar:O, on:O, Human:O, Rights:O, ”:O, ,:O, and:O, “:O, Ukraine:S-LOC, ’s:O, Search:O, for:O, a:O, Regional:O, Foreign:O, Policy: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: 'The targets of these campaigns , according to Kaspersky , were located variously in Belgium , Hungary , Luxembourg and Spain .' [/INST] [The:O, targets:O, of:O, these:O, campaigns:O, ,:O, according:O, to:O, Kaspersky:S-SECTEAM, ,:O, were:O, located:O, variously:O, in:O, Belgium:S-LOC, ,:O, Hungary:S-LOC, ,:O, Luxembourg:S-LOC, and:O, Spain: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: 'Kaspersky goes on to state that by obtaining log files from the MiniDuke command and control servers , they were able to identify high-profile victims from Ukraine , Belgium , Portugal , Romania , the Czech Republic , Ireland , the United States and Hungary .' [/INST] [Kaspersky:S-SECTEAM, goes:O, on:O, to:O, state:O, that:O, by:O, obtaining:O, log:O, files:O, from:O, the:O, MiniDuke:S-MAL, command:B-TOOL, and:I-TOOL, control:E-TOOL, servers:O, ,:O, they:O, were:O, able:O, to:O, identify:O, high-profile:O, victims:O, from:O, Ukraine:S-LOC, ,:O, Belgium:S-LOC, ,:O, Portugal:S-LOC, ,:O, Romania:S-LOC, ,:O, the:O, Czech:B-LOC, Republic:E-LOC, ,:O, Ireland:S-LOC, ,:O, the:O, United:B-LOC, States:E-LOC, and:O, Hungary: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: 'After the February campaigns , MiniDuke activity appeared to quiet down , although it did not fully stop , for the rest of 2013 .' [/INST] [After:O, the:O, February:S-TIME, campaigns:O, ,:O, MiniDuke:S-MAL, activity:O, appeared:O, to:O, quiet:O, down:O, ,:O, although:O, it:O, did:O, not:O, fully:O, stop:O, ,:O, for:O, the:O, rest:B-TIME, of:I-TIME, 2013: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: 'The Dukes group as a whole however showed no sign of slowing down .' [/INST] [The:O, Dukes:S-APT, group:O, as:O, a:O, whole:O, however:O, showed:O, no:O, sign:O, of:O, slowing:O, down: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 fact , we saw yet another Duke malware toolset , OnionDuke , appear first in 2013 .' [/INST] [In:O, fact:O, ,:O, we:O, saw:O, yet:O, another:O, Duke:S-APT, malware:O, toolset:O, ,:O, OnionDuke:S-MAL, ,:O, appear:O, first:O, in:O, 2013: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: 'Like CozyDuke , OnionDuke appears to have been designed with versatility in mind , and takes a similarly modular platform approach .' [/INST] [Like:O, CozyDuke:S-MAL, ,:O, OnionDuke:S-MAL, appears:O, to:O, have:O, been:O, designed:O, with:O, versatility:O, in:O, mind:O, ,:O, and:O, takes:O, a:O, similarly:O, modular:O, platform:O, approach: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 OnionDuke toolset includes various modules for purposes such as password stealing , information gathering , denial of service ( DoS ) attacks , and even posting spam to the Russian social media network , VKontakte .' [/INST] [The:O, OnionDuke:S-MAL, toolset:O, includes:O, various:O, modules:O, for:O, purposes:O, such:O, as:O, password:O, stealing:O, ,:O, information:O, gathering:O, ,:O, denial:B-TOOL, of:I-TOOL, service:E-TOOL, (:O, DoS:S-TOOL, ):O, attacks:O, ,:O, and:O, even:O, posting:O, spam:O, to:O, the:O, Russian:O, social:O, media:O, network:O, ,:O, VKontakte: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: 'The OnionDuke toolset also includes a dropper , an information stealer variant and multiple distinct versions of the core component that is responsible for interacting with the various modules .' [/INST] [The:O, OnionDuke:S-MAL, toolset:O, also:O, includes:O, a:O, dropper:O, ,:O, an:O, information:O, stealer:O, variant:O, and:O, multiple:O, distinct:O, versions:O, of:O, the:O, core:O, component:O, that:O, is:O, responsible:O, for:O, interacting:O, with:O, the:O, various:O, modules: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 OnionDuke especially curious is an infection vector it began using during the summer of 2013 .' [/INST] [What:O, makes:O, OnionDuke:S-MAL, especially:O, curious:O, is:O, an:O, infection:O, vector:O, it:O, began:O, using:O, during:O, the:O, summer:B-TIME, of:I-TIME, 2013: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: 'To spread the toolset , the Dukes used a wrapper to combine OnionDuke with legitimate applications , created torrent files containing these trojanized applications , then uploaded them to websites hosting torrent files .' [/INST] [To:O, spread:O, the:O, toolset:O, ,:O, the:O, Dukes:S-APT, used:O, a:O, wrapper:O, to:O, combine:O, OnionDuke:S-MAL, with:O, legitimate:O, applications:O, ,:O, created:O, torrent:O, files:O, containing:O, these:O, trojanized:O, applications:O, ,:O, then:O, uploaded:O, them:O, to:O, websites:O, hosting:O, torrent:O, files: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: 'Victims who used the torrent files to download the applications would end up getting infected with OnionDuke .' [/INST] [Victims:O, who:O, used:O, the:O, torrent:O, files:O, to:O, download:O, the:O, applications:O, would:O, end:O, up:O, getting:O, infected:O, with:O, OnionDuke: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: 'For most of the OnionDuke components we observed , the first versions that we are aware of were compiled during the summer of 2013 , suggesting that this was a period of active development around this toolset .' [/INST] [For:O, most:O, of:O, the:O, OnionDuke:S-MAL, components:O, we:O, observed:O, ,:O, the:O, first:O, versions:O, that:O, we:O, are:O, aware:O, of:O, were:O, compiled:O, during:O, the:O, summer:B-TIME, of:I-TIME, 2013:E-TIME, ,:O, suggesting:O, that:O, this:O, was:O, a:O, period:O, of:O, active:O, development:O, around:O, this: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: 'Critically however , the first sample of the OnionDuke dropper , which we have observed being used only with components of this toolset , was compiled on the 17th of February 2013 .' [/INST] [Critically:O, however:O, ,:O, the:O, first:O, sample:O, of:O, the:O, OnionDuke:S-MAL, dropper:O, ,:O, which:O, we:O, have:O, observed:O, being:O, used:O, only:O, with:O, components:O, of:O, this:O, toolset:O, ,:O, was:O, compiled:O, on:O, the:O, 17th:B-TIME, of:I-TIME, February:I-TIME, 2013: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 is significant because it suggests that OnionDuke was under development before any part of the Duke operation became public .' [/INST] [This:O, is:O, significant:O, because:O, it:O, suggests:O, that:O, OnionDuke:S-MAL, was:O, under:O, development:O, before:O, any:O, part:O, of:O, the:O, Duke:S-APT, operation:O, became:O, public: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: 'OnionDuke ’s development therefore could not have been simply a response to the outing of one of the other Duke malware , but was instead intended for use alongside the other toolsets .' [/INST] [OnionDuke:S-MAL, ’s:O, development:O, therefore:O, could:O, not:O, have:O, been:O, simply:O, a:O, response:O, to:O, the:O, outing:O, of:O, one:O, of:O, the:O, other:O, Duke:S-APT, malware:O, ,:O, but:O, was:O, instead:O, intended:O, for:O, use:O, alongside:O, the:O, other:O, toolsets: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 indication that the Dukes planned to use an arsenal of 5 malware toolsets in parallel suggests that they were operating with both significant resources and capacity .' [/INST] [This:O, indication:O, that:O, the:O, Dukes:S-APT, planned:O, to:O, use:O, an:O, arsenal:O, of:O, 5:O, malware:O, toolsets:O, in:O, parallel:O, suggests:O, that:O, they:O, were:O, operating:O, with:O, both:O, significant:O, resources:O, and:O, capacity: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 2013 , many of the decoy documents employed by the Dukes in their campaigns were related to Ukraine ; examples include a letter undersigned by the First Deputy Minister for Foreign Affairs of Ukraine , a letter from the embassy of the Netherlands in Ukraine to the Ukrainian Ministry of Foreign affairs and a document titled “ Ukraine ’s Search for a Regional Foreign Policy ” .' [/INST] [In:O, 2013:S-TIME, ,:O, many:O, of:O, the:O, decoy:O, documents:O, employed:O, by:O, the:O, Dukes:S-APT, in:O, their:O, campaigns:O, were:O, related:O, to:O, Ukraine:S-LOC, ;:O, examples:O, include:O, a:O, letter:O, undersigned:O, by:O, the:O, First:O, Deputy:O, Minister:O, for:O, Foreign:B-IDTY, Affairs:E-IDTY, of:O, Ukraine:S-LOC, ,:O, a:O, letter:O, from:O, the:O, embassy:O, of:O, the:O, Netherlands:S-LOC, in:O, Ukraine:S-LOC, to:O, the:O, Ukrainian:B-IDTY, Ministry:E-IDTY, of:O, Foreign:B-IDTY, affairs:E-IDTY, and:O, a:O, document:O, titled:O, “:O, Ukraine:S-LOC, ’s:O, Search:O, for:O, a:O, Regional:O, Foreign:O, Policy: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: 'These decoy documents however were written before the start of the November 2013 Euromaidan protests in Ukraine and the subsequent upheaval .' [/INST] [These:O, decoy:O, documents:O, however:O, were:O, written:O, before:O, the:O, start:B-TIME, of:I-TIME, the:I-TIME, November:I-TIME, 2013:E-TIME, Euromaidan:S-ACT, protests:O, in:O, Ukraine:S-APT, and:O, the:O, subsequent:O, upheaval: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 therefore important to note that , contrary to what might be assumed , we have actually observed a drop instead of an increase in Ukraine related campaigns from the Dukes following the country ’s political crisis .' [/INST] [It:O, is:O, therefore:O, important:O, to:O, note:O, that:O, ,:O, contrary:O, to:O, what:O, might:O, be:O, assumed:O, ,:O, we:O, have:O, actually:O, observed:O, a:O, drop:O, instead:O, of:O, an:O, increase:O, in:O, Ukraine:S-LOC, related:O, campaigns:O, from:O, the:O, Dukes:S-APT, following:O, the:O, country:O, ’s:O, political:O, crisis: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 is in stark contrast to some other suspected Russian threat actors ( such as Operation Pawn Storm ) who appear to have increased their targeting of Ukraine following the crisis .' [/INST] [This:O, is:O, in:O, stark:O, contrast:O, to:O, some:O, other:O, suspected:O, Russian:O, threat:O, actors:O, (:O, such:O, as:O, Operation:B-ACT, Pawn:I-ACT, Storm:E-ACT, ):O, who:O, appear:O, to:O, have:O, increased:O, their:O, targeting:O, of:O, Ukraine:O, following:O, the:O, crisis: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 supports our analysis that the overarching theme in the Dukes ’ targeting is the collection of intelligence to support diplomatic efforts .' [/INST] [This:O, supports:O, our:O, analysis:O, that:O, the:O, overarching:O, theme:O, in:O, the:O, Dukes:S-APT, ’:O, targeting:O, is:O, the:O, collection:O, of:O, intelligence:O, to:O, support:O, diplomatic:O, efforts: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 Dukes actively targeted Ukraine before the crisis , at a time when Russia was still weighing her options , but once Russia moved from diplomacy to direct action , Ukraine was no longer relevant to the Dukes in the same way .' [/INST] [The:O, Dukes:S-APT, actively:O, targeted:O, Ukraine:S-LOC, before:O, the:O, crisis:O, ,:O, at:O, a:O, time:O, when:O, Russia:S-LOC, was:O, still:O, weighing:O, her:O, options:O, ,:O, but:O, once:O, Russia:S-LOC, moved:O, from:O, diplomacy:O, to:O, direct:O, action:O, ,:O, Ukraine:S-LOC, was:O, no:O, longer:O, relevant:O, to:O, the:O, Dukes:S-APT, in:O, the:O, same:O, way: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 surprising turn of events , in September 2013 a CosmicDuke campaign was observed targeting Russian speakers involved in the trade of illegal and controlled substances .' [/INST] [In:O, a:O, surprising:O, turn:O, of:O, events:O, ,:O, in:O, September:B-TIME, 2013:E-TIME, a:O, CosmicDuke:S-MAL, campaign:O, was:O, observed:O, targeting:O, Russian:O, speakers:O, involved:O, in:O, the:O, trade:O, of:O, illegal:O, and:O, controlled:O, substances: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: 'Kaspersky Labs , who sometimes refer to CosmicDuke as ‘ Bot Gen Studio ’ , speculated that “ one possibility is that ‘ Bot Gen Studio ’ is a malware platform also available as a so-called ‘ legal spyware ’ tool ” ;' [/INST] [Kaspersky:B-SECTEAM, Labs:E-SECTEAM, ,:O, who:O, sometimes:O, refer:O, to:O, CosmicDuke:S-MAL, as:O, ‘:O, Bot:O, Gen:O, Studio:O, ’:O, ,:O, speculated:O, that:O, “:O, one:O, possibility:O, is:O, that:O, ‘:O, Bot:O, Gen:O, Studio:O, ’:O, is:O, a:O, malware:O, platform:O, also:O, available:O, as:O, a:O, so-called:O, ‘:O, legal:O, spyware:O, ’:O, tool: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: 'therefore , those using CosmicDuke to target drug dealers and those targeting governments are two separate entities .' [/INST] [therefore:O, ,:O, those:O, using:O, CosmicDuke:S-MAL, to:O, target:O, drug:O, dealers:O, and:O, those:O, targeting:O, governments:O, are:O, two:O, separate:O, entities: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 however feel it is unlikely that the CosmicDuke operators targeting drug dealers and those targeting governments could be two entirely independent entities .' [/INST] [We:O, however:O, feel:O, it:O, is:O, unlikely:O, that:O, the:O, CosmicDuke:S-MAL, operators:O, targeting:O, drug:O, dealers:O, and:O, those:O, targeting:O, governments:O, could:O, be:O, two:O, entirely:O, independent:O, entities: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 shared supplier of malware would explain the overlap in tools , but it would not explain the significant overlap we have also observed in operational techniques related to command and control infrastructure .' [/INST] [A:O, shared:O, supplier:O, of:O, malware:O, would:O, explain:O, the:O, overlap:O, in:O, tools:O, ,:O, but:O, it:O, would:O, not:O, explain:O, the:O, significant:O, overlap:O, we:O, have:O, also:O, observed:O, in:O, operational:O, techniques:O, related:O, to:O, command:O, and:O, control:O, infrastructure: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 feel the targeting of drug dealers was a new task for a subset of the Dukes group , possibly due to the drug trade ’s relevance to security policy issues .' [/INST] [Instead:O, ,:O, we:O, feel:O, the:O, targeting:O, of:O, drug:O, dealers:O, was:O, a:O, new:O, task:O, for:O, a:O, subset:O, of:O, the:O, Dukes:S-APT, group:O, ,:O, possibly:O, due:O, to:O, the:O, drug:O, trade:O, ’s:O, relevance:O, to:O, security:O, policy:O, issues: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 also believe the tasking to have been temporary , because we have not observed any further similar targeting from the Dukes after the spring of 2014 .' [/INST] [We:O, also:O, believe:O, the:O, tasking:O, to:O, have:O, been:O, temporary:O, ,:O, because:O, we:O, have:O, not:O, observed:O, any:O, further:O, similar:O, targeting:O, from:O, the:O, Dukes:S-APT, after:O, the:O, spring:B-TIME, of:I-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: 'While MiniDuke activity decreased significantly during the rest of 2013 following the attention it garnered from researchers , the beginning of 2014 saw the toolset back in full force .' [/INST] [While:O, MiniDuke:S-MAL, activity:O, decreased:O, significantly:O, during:O, the:O, rest:B-TIME, of:I-TIME, 2013:E-TIME, following:O, the:O, attention:O, it:O, garnered:O, from:O, researchers:O, ,:O, the:O, beginning:B-TIME, of:I-TIME, 2014:E-TIME, saw:O, the:O, toolset:O, back:O, in:O, full:O, force: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: 'All MiniDuke components , from the loader and downloader to the backdoor , had been slightly updated and modified during the downtime .' [/INST] [All:O, MiniDuke:S-MAL, components:O, ,:O, from:O, the:O, loader:O, and:O, downloader:O, to:O, the:O, backdoor:O, ,:O, had:O, been:O, slightly:O, updated:O, and:O, modified:O, during:O, the:O, downtime: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: 'Interestingly , the nature of these modifications suggests that their primary purpose was to regain the element of stealth and undetectability that had been lost almost a year earlier .' [/INST] [Interestingly:O, ,:O, the:O, nature:O, of:O, these:O, modifications:O, suggests:O, that:O, their:O, primary:O, purpose:O, was:O, to:O, regain:O, the:O, element:O, of:O, stealth:O, and:O, undetectability:O, that:O, had:O, been:O, lost:O, almost:O, a:O, year:O, earlier: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 these modifications , arguably the most important were the ones done to the loader .' [/INST] [Of:O, these:O, modifications:O, ,:O, arguably:O, the:O, most:O, important:O, were:O, the:O, ones:O, done:O, to:O, the:O, loader: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: 'These resulted in a loader version that would later become known as the “ Nemesis Gemina loader ” due to PDB strings found in many of the samples .' [/INST] [These:O, resulted:O, in:O, a:O, loader:O, version:O, that:O, would:O, later:O, become:O, known:O, as:O, the:O, “:O, Nemesis:B-MAL, Gemina:I-MAL, loader:E-MAL, ”:O, due:O, to:O, PDB:S-TOOL, strings:O, found:O, in:O, many:O, of:O, the:O, samples: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 however still only an iteration on earlier versions of the MiniDuke loader .' [/INST] [It:O, is:O, however:O, still:O, only:O, an:O, iteration:O, on:O, earlier:O, versions:O, of:O, the:O, MiniDuke:S-MAL, loader: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 first observed samples of the Nemesis Gemina loader ( compiled on 14th December 2013 ) were used to load the updated MiniDuke backdoor , but by the spring of 2014 the Nemesis Gemina loader was also observed in use with CosmicDuke .' [/INST] [The:O, first:O, observed:O, samples:O, of:O, the:O, Nemesis:B-MAL, Gemina:I-MAL, loader:E-MAL, (:O, compiled:O, on:O, 14th:B-TIME, December:I-TIME, 2013:E-TIME, ):O, were:O, used:O, to:O, load:O, the:O, updated:O, MiniDuke:B-MAL, backdoor:E-MAL, ,:O, but:O, by:O, the:O, spring:B-TIME, of:I-TIME, 2014:E-TIME, the:O, Nemesis:B-MAL, Gemina:I-MAL, loader:E-MAL, was:O, also:O, observed:O, in:O, use: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: 'Following the MiniDuke expose , CosmicDuke in turn got its moment of fame when F-Secure published a whitepaper about it on 2nd July 2014 .' [/INST] [Following:O, the:O, MiniDuke:S-MAL, expose:O, ,:O, CosmicDuke:S-MAL, in:O, turn:O, got:O, its:O, moment:O, of:O, fame:O, when:O, F-Secure:S-SECTEAM, published:O, a:O, whitepaper:O, about:O, it:O, on:O, 2nd:B-TIME, July:I-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: 'The next day , Kaspersky also published their own research on the malware .' [/INST] [The:O, next:O, day:O, ,:O, Kaspersky:S-SECTEAM, also:O, published:O, their:O, own:O, research:O, on:O, the:O, malware: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 should be noted that until this point , even though CosmicDuke had been in active use for over 4 years , and had undergone minor modifications and updates during that time , even the most recent CosmicDuke samples would often embed persistence components that date back to 2012 .' [/INST] [It:O, should:O, be:O, noted:O, that:O, until:O, this:O, point:O, ,:O, even:O, though:O, CosmicDuke:S-MAL, had:O, been:O, in:O, active:O, use:O, for:O, over:O, 4:O, years:O, ,:O, and:O, had:O, undergone:O, minor:O, modifications:O, and:O, updates:O, during:O, that:O, time:O, ,:O, even:O, the:O, most:O, recent:O, CosmicDuke:S-MAL, samples:O, would:O, often:O, embed:O, persistence:O, components:O, that:O, date:O, back:O, to:O, 2012: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: 'These samples would also contain artefacts of functionality from the earliest CosmicDuke samples from 2010 .' [/INST] [These:O, samples:O, would:O, also:O, contain:O, artefacts:O, of:O, functionality:O, from:O, the:O, earliest:O, CosmicDuke:S-MAL, samples:O, from:O, 2010: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: 'It is therefore valuable to observe how the Dukes reacted to CosmicDuke ’s outing at the beginning of July .' [/INST] [It:O, is:O, therefore:O, valuable:O, to:O, observe:O, how:O, the:O, Dukes:O, reacted:O, to:O, CosmicDuke:S-MAL, ’s:O, outing:O, at:O, the:O, beginning:O, of:O, July: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: 'By the end of that month , CosmicDuke samples we found that had been compiled on the 30th of July had shed unused parts of their code that had essentially just been relics of the past .' [/INST] [By:O, the:O, end:O, of:O, that:O, month:O, ,:O, CosmicDuke:S-MAL, samples:O, we:O, found:O, that:O, had:O, been:O, compiled:O, on:O, the:O, 30th:B-TIME, of:I-TIME, July:E-TIME, had:O, shed:O, unused:O, parts:O, of:O, their:O, code:O, that:O, had:O, essentially:O, just:O, been:O, relics:O, of:O, the:O, past: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: 'Similarly , some of the hardcoded values that had remained unaltered in CosmicDuke samples for many years had been changed .' [/INST] [Similarly:O, ,:O, some:O, of:O, the:O, hardcoded:O, values:O, that:O, had:O, remained:O, unaltered:O, in:O, CosmicDuke:S-MAL, samples:O, for:O, many:O, years:O, had:O, been:O, changed: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 these edits were an attempt at evading detection by modifying or removing parts of the toolset that the authors believed might be helpful in identifying and detecting it .' [/INST] [We:O, believe:O, these:O, edits:O, were:O, an:O, attempt:O, at:O, evading:O, detection:O, by:O, modifying:O, or:O, removing:O, parts:O, of:O, the:O, toolset:O, that:O, the:O, authors:O, believed:O, might:O, be:O, helpful:O, in:O, identifying:O, and:O, detecting:O, it: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: 'Concurrently with the alterations to CosmicDuke , the Dukes were also hard at work modifying their trusted loader .' [/INST] [Concurrently:O, with:O, the:O, alterations:O, to:O, CosmicDuke:S-MAL, ,:O, the:O, Dukes:S-APT, were:O, also:O, hard:O, at:O, work:O, modifying:O, their:O, trusted:O, loader: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 CosmicDuke toolset , the loader used by both MiniDuke and CosmicDuke had previously only undergone one major update ( the Nemesis Gemina upgrade ) since the first known samples from 2010 .' [/INST] [Much:O, like:O, the:O, CosmicDuke:S-MAL, toolset:O, ,:O, the:O, loader:O, used:O, by:O, both:O, MiniDuke:S-MAL, and:O, CosmicDuke:S-MAL, had:O, previously:O, only:O, undergone:O, one:O, major:O, update:O, (:O, the:O, Nemesis:B-MAL, Gemina:E-MAL, upgrade:O, ):O, since:O, the:O, first:O, known:O, samples:O, from:O, 2010: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: 'Again , much of the modification work focused on removing redundant code in an attempt to appear different from earlier versions of the loader .' [/INST] [Again:O, ,:O, much:O, of:O, the:O, modification:O, work:O, focused:O, on:O, removing:O, redundant:O, code:O, in:O, an:O, attempt:O, to:O, appear:O, different:O, from:O, earlier:O, versions:O, of:O, the:O, loader: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: 'Interestingly however , another apparent evasion trick was also attempted - forging of the loaders ’ compilation timestamps .' [/INST] [Interestingly:O, however:O, ,:O, another:O, apparent:O, evasion:O, trick:O, was:O, also:O, attempted:O, -:O, forging:O, of:O, the:O, loaders:O, ’:O, compilation:O, timestamps: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 first CosmicDuke sample we observed after the initial research on CosmicDuke was a sample compiled on the 30th of July 2014 .' [/INST] [The:O, first:O, CosmicDuke:S-MAL, sample:O, we:O, observed:O, after:O, the:O, initial:O, research:O, on:O, CosmicDuke:S-MAL, was:O, a:O, sample:O, compiled:O, on:O, the:O, 30th:B-TIME, of:I-TIME, July:I-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: 'The loader used by the sample purported to have been compiled on the 25th of March 2010 .' [/INST] [The:O, loader:O, used:O, by:O, the:O, sample:O, purported:O, to:O, have:O, been:O, compiled:O, on:O, the:O, 25th:B-TIME, of:I-TIME, March:I-TIME, 2010: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: 'Due to artefacts left in the loader during compilation time however , we know that it used a specific version of the Boost library , 1.54.0 , that was only published on the 1st of July 2013 .' [/INST] [Due:O, to:O, artefacts:O, left:O, in:O, the:O, loader:O, during:O, compilation:O, time:O, however:O, ,:O, we:O, know:O, that:O, it:O, used:O, a:O, specific:O, version:O, of:O, the:O, Boost:S-TOOL, library:O, ,:O, 1.54.0:O, ,:O, that:O, was:O, only:O, published:O, on:O, the:O, 1st:B-TIME, of:I-TIME, July:I-TIME, 2013: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: 'The compilation timestamp therefore had to have been faked .' [/INST] [The:O, compilation:O, timestamp:O, therefore:O, had:O, to:O, have:O, been:O, faked: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: 'F-Secure ’s whitepaper on CosmicDuke includes a timeline of the loader ’s usage , based on compilation timestamps .' [/INST] [F-Secure:S-SECTEAM, ’s:O, whitepaper:O, on:O, CosmicDuke:S-MAL, includes:O, a:O, timeline:O, of:O, the:O, loader:O, ’s:O, usage:O, ,:O, based:O, on:O, compilation:O, timestamps: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: 'Perhaps the Dukes group thought that by faking a timestamp from before the earliest one cited in the whitepaper , they might be able to confuse researchers .' [/INST] [Perhaps:O, the:O, Dukes:S-APT, group:O, thought:O, that:O, by:O, faking:O, a:O, timestamp:O, from:O, before:O, the:O, earliest:O, one:O, cited:O, in:O, the:O, whitepaper:O, ,:O, they:O, might:O, be:O, able:O, to:O, confuse:O, researchers: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: 'During the rest of 2014 and the spring of 2015 , the Dukes continued making similar evasionfocused modifications to CosmicDuke , as well as experimenting with ways to obfuscate the loader .' [/INST] [During:O, the:O, rest:B-TIME, of:I-TIME, 2014:E-TIME, and:O, the:O, spring:B-TIME, of:I-TIME, 2015:E-TIME, ,:O, the:O, Dukes:S-APT, continued:O, making:O, similar:O, evasionfocused:O, modifications:O, to:O, CosmicDuke:S-MAL, ,:O, as:O, well:O, as:O, experimenting:O, with:O, ways:O, to:O, obfuscate:O, the:O, loader: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 latter case however , the group appear to have also simultaneously developed an entirely new loader , which we first observed being used in conjunction with CosmicDuke during the spring of 2015 .' [/INST] [In:O, the:O, latter:O, case:O, however:O, ,:O, the:O, group:O, appear:O, to:O, have:O, also:O, simultaneously:O, developed:O, an:O, entirely:O, new:O, loader:O, ,:O, which:O, we:O, first:O, observed:O, being:O, used:O, in:O, conjunction:O, with:O, CosmicDuke:S-MAL, during:O, the:O, spring:B-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: 'While it is not surprising that the Dukes reacted to multiple companies publishing extensive reports on one of their key toolsets , it is valuable to note the manner in which they responded .' [/INST] [While:O, it:O, is:O, not:O, surprising:O, that:O, the:O, Dukes:S-APT, reacted:O, to:O, multiple:O, companies:O, publishing:O, extensive:O, reports:O, on:O, one:O, of:O, their:O, key:O, toolsets:O, ,:O, it:O, is:O, valuable:O, to:O, note:O, the:O, manner:O, in:O, which:O, they:O, responded: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 MiniDuke expose in February 2013 , the Dukes again appeared to prioritize continuing operations over staying hidden .' [/INST] [Much:O, like:O, the:O, MiniDuke:S-MAL, expose:O, in:O, February:B-TIME, 2013:E-TIME, ,:O, the:O, Dukes:S-APT, again:O, appeared:O, to:O, prioritize:O, continuing:O, operations:O, over:O, staying:O, hidden: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 could have ceased all use of CosmicDuke ( at least until they had developed a new loader ) or retired it entirely , since they still had other toolsets available .' [/INST] [They:O, could:O, have:O, ceased:O, all:O, use:O, of:O, CosmicDuke:S-MAL, (:O, at:O, least:O, until:O, they:O, had:O, developed:O, a:O, new:O, loader:O, ):O, or:O, retired:O, it:O, entirely:O, ,:O, since:O, they:O, still:O, had:O, other:O, toolsets:O, available: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 , they opted for minimal downtime and attempted to continue operations , with only minor modifications to the toolset .' [/INST] [Instead:O, ,:O, they:O, opted:O, for:O, minimal:O, downtime:O, and:O, attempted:O, to:O, continue:O, operations:O, ,:O, with:O, only:O, minor:O, modifications:O, to:O, the: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: 'While we now know that CozyDuke had been under development since at least the end of 2011 , it was not until the early days of July 2014 that the first large-scale CozyDuke campaign that we are aware of took place .' [/INST] [While:O, we:O, now:O, know:O, that:O, CozyDuke:S-MAL, had:O, been:O, under:O, development:O, since:O, at:O, least:O, the:O, end:B-TIME, of:I-TIME, 2011:E-TIME, ,:O, it:O, was:O, not:O, until:O, the:O, early:O, days:O, of:O, July:B-TIME, 2014:E-TIME, that:O, the:O, first:O, large-scale:O, CozyDuke:S-MAL, campaign:O, that:O, we:O, are:O, aware:O, of:O, took:O, place: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 campaign , like later CozyDuke campaigns , began with spear-phishing emails that tried to impersonate commonly seen spam emails .' [/INST] [This:O, campaign:O, ,:O, like:O, later:O, CozyDuke:S-MAL, campaigns:O, ,:O, began:O, with:O, spear-phishing:S-ACT, emails:S-TOOL, that:O, tried:O, to:O, impersonate:O, commonly:O, seen:O, spam:O, emails: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: 'These spear-phishing emails would contain links that eventually lead the victim to becoming infected with CozyDuke .' [/INST] [These:O, spear-phishing:S-ACT, emails:S-TOOL, would:O, contain:O, links:O, that:O, eventually:O, lead:O, the:O, victim:O, to:O, becoming:O, infected:O, with:O, CozyDuke: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: 'Some of the CozyDuke spear-phishing emails from early July posed as e-fax arrival notifications , a popular theme for spam emails , and used the same “ US letter fax test page ” decoy document that was used a year later by CloudDuke .' [/INST] [Some:O, of:O, the:O, CozyDuke:S-MAL, spear-phishing:S-ACT, emails:S-TOOL, from:O, early:O, July:S-TIME, posed:O, as:O, e-fax:S-TOOL, arrival:O, notifications:O, ,:O, a:O, popular:O, theme:O, for:O, spam:O, emails:S-TOOL, ,:O, and:O, used:O, the:O, same:O, “:O, US:S-LOC, letter:O, fax:S-TOOL, test:O, page:O, ”:O, decoy:O, document:O, that:O, was:O, used:O, a:O, year:O, later:O, by: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 at least one case however , the email instead contained a link to a zip archive file named “ Office Monkeys LOL Video.zip ” , which was hosted on the DropBox cloud storage service .' [/INST] [In:O, at:O, least:O, one:O, case:O, however:O, ,:O, the:O, email:S-TOOL, instead:O, contained:O, a:O, link:O, to:O, a:O, zip:S-TOOL, archive:O, file:O, named:O, “:O, Office:B-FILE, Monkeys:I-FILE, LOL:I-FILE, Video.zip:E-FILE, ”:O, ,:O, which:O, was:O, hosted:O, on:O, the:O, DropBox:S-TOOL, cloud:O, storage:O, service: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 made this particular case interesting was that instead of the usual dull PDF file , the decoy was a Flash video file , more specifically a Super Bowl advertisement from 2007 purporting to show monkeys at an office .' [/INST] [What:O, made:O, this:O, particular:O, case:O, interesting:O, was:O, that:O, instead:O, of:O, the:O, usual:O, dull:O, PDF:S-TOOL, file:O, ,:O, the:O, decoy:O, was:O, a:O, Flash:S-TOOL, video:O, file:O, ,:O, more:O, specifically:O, a:O, Super:O, Bowl:O, advertisement:O, from:O, 2007:S-TIME, purporting:O, to:O, show:O, monkeys:O, at:O, an:O, office: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 DUKES 7 YEARS OF RUSSIAN CYBERESPIONAGE .' [/INST] [THE:O, DUKES:S-APT, 7:O, YEARS:O, OF:O, RUSSIAN:O, CYBERESPIONAGE: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: 'Release_Time : 2015-09' [/INST] [Release_Time:O, ::O, 2015-09: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: 'Report_URL : https://blog-assets.f-secure.com/wp-content/uploads/2020/03/18122307/F-Secure_Dukes_Whitepaper.pdf' [/INST] [Report_URL:O, ::O, https://blog-assets.f-secure.com/wp-content/uploads/2020/03/18122307/F-Secure_Dukes_Whitepaper.pdf: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: '2014 : OnionDuke gets caught using a malicious Tor node .' [/INST] [2014:S-TIME, ::O, OnionDuke:S-MAL, gets:O, caught:O, using:O, a:O, malicious:O, Tor:S-TOOL, node: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: 'On the 23rd of October 2014 , Leviathan Security Group published a blog post describing a malicious Tor exit node they had found .' [/INST] [On:O, the:O, 23rd:B-TIME, of:I-TIME, October:I-TIME, 2014:E-TIME, ,:O, Leviathan:B-SECTEAM, Security:I-SECTEAM, Group:E-SECTEAM, published:O, a:O, blog:O, post:O, describing:O, a:O, malicious:O, Tor:S-TOOL, exit:O, node:O, they:O, had:O, found: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 noted that this node appeared to be maliciously modifying any executables that were downloaded through it over a HTTP connection .' [/INST] [They:O, noted:O, that:O, this:O, node:O, appeared:O, to:O, be:O, maliciously:O, modifying:O, any:O, executables:O, that:O, were:O, downloaded:O, through:O, it:O, over:O, a:O, HTTP:S-PROT, connection: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: 'Executing the modified applications obtained this way would result in the victim being infected with unidentified malware .' [/INST] [Executing:O, the:O, modified:O, applications:O, obtained:O, this:O, way:O, would:O, result:O, in:O, the:O, victim:O, being:O, infected:O, with:O, unidentified:O, malware: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: 'On the 14th of November , F-Secure published a blog post naming the malware OnionDuke and associating it with MiniDuke and CosmicDuke , the other Duke toolsets known at the time .' [/INST] [On:O, the:O, 14th:B-TIME, of:I-TIME, November:E-TIME, ,:O, F-Secure:S-SECTEAM, published:O, a:O, blog:O, post:O, naming:O, the:O, malware:O, OnionDuke:S-MAL, and:O, associating:O, it:O, with:O, MiniDuke:S-MAL, and:O, CosmicDuke:S-MAL, ,:O, the:O, other:O, Duke:S-APT, toolsets:O, known:O, at:O, the:O, 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: 'Based on our investigations into OnionDuke , we believe that for about 7 months , from April 2014 to when Leviathan published their blog post in October 2014 , the Tor exit node identified by the researchers was being used to wrap executables on-the-fly with OnionDuke ( image 7 , page 13 ) .' [/INST] [Based:O, on:O, our:O, investigations:O, into:O, OnionDuke:S-MAL, ,:O, we:O, believe:O, that:O, for:O, about:O, 7:O, months:O, ,:O, from:O, April:B-TIME, 2014:E-TIME, to:O, when:O, Leviathan:S-SECTEAM, published:O, their:O, blog:O, post:O, in:O, October:B-TIME, 2014:E-TIME, ,:O, the:O, Tor:S-TOOL, exit:O, node:O, identified:O, by:O, the:O, researchers:O, was:O, being:O, used:O, to:O, wrap:O, executables:O, on-the-fly:O, with:O, OnionDuke:S-MAL, (:O, image:O, 7:O, ,:O, page:O, 13: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: 'This is similar to the way in which the toolset was being spread via trojanized applications in torrent files during the summer of 2013 .' [/INST] [This:O, is:O, similar:O, to:O, the:O, way:O, in:O, which:O, the:O, toolset:O, was:O, being:O, spread:O, via:O, trojanized:O, applications:O, in:O, torrent:O, files:O, during:O, the:O, summer:B-TIME, of:I-TIME, 2013: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: 'While investigating the OnionDuke variant being spread by the malicious Tor node , we also identified another OnionDuke variant that appeared to have successfully compromised multiple victims in the ministry of foreign affairs of an Eastern European country during the spring of 2014 .' [/INST] [While:O, investigating:O, the:O, OnionDuke:S-MAL, variant:O, being:O, spread:O, by:O, the:O, malicious:O, Tor:S-TOOL, node:O, ,:O, we:O, also:O, identified:O, another:O, OnionDuke:S-MAL, variant:O, that:O, appeared:O, to:O, have:O, successfully:O, compromised:O, multiple:O, victims:O, in:O, the:O, ministry:O, of:O, foreign:O, affairs:O, of:O, an:O, Eastern:O, European:O, country:O, during:O, the:O, spring:B-TIME, of:I-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: 'This variant differed significantly in functionality from the one being spread via the Tor node , further suggesting that different OnionDuke variants are intended for different kinds of victims .' [/INST] [This:O, variant:O, differed:O, significantly:O, in:O, functionality:O, from:O, the:O, one:O, being:O, spread:O, via:O, the:O, Tor:S-TOOL, node:O, ,:O, further:O, suggesting:O, that:O, different:O, OnionDuke:S-MAL, variants:O, are:O, intended:O, for:O, different:O, kinds:O, of: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: 'We believe that , unusually , the purpose of the OnionDuke variant spread via the Tor node was not to pursue targeted attacks but instead to form a small botnet for later use .' [/INST] [We:O, believe:O, that:O, ,:O, unusually:O, ,:O, the:O, purpose:O, of:O, the:O, OnionDuke:S-MAL, variant:O, spread:O, via:O, the:O, Tor:S-TOOL, node:O, was:O, not:O, to:O, pursue:O, targeted:O, attacks:O, but:O, instead:O, to:O, form:O, a:O, small:O, botnet:O, for:O, later:O, use: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 OnionDuke variant is related to the one seen during the summer of 2013 being spread via torrent files .' [/INST] [This:O, OnionDuke:S-MAL, variant:O, is:O, related:O, to:O, the:O, one:O, seen:O, during:O, the:O, summer:B-TIME, of:I-TIME, 2013:E-TIME, being:O, spread:O, via:O, torrent:O, files: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 of these infection vectors are highly indiscriminate and untargeted when compared to spearphishing , the usual infection vector of choice for the Dukes .' [/INST] [Both:O, of:O, these:O, infection:O, vectors:O, are:O, highly:O, indiscriminate:O, and:O, untargeted:O, when:O, compared:O, to:O, spearphishing:S-ACT, ,:O, the:O, usual:O, infection:O, vector:O, of:O, choice:O, for: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: 'Further , the functionality of the OnionDuke variant is derived from a number of modules .' [/INST] [Further:O, ,:O, the:O, functionality:O, of:O, the:O, OnionDuke:S-MAL, variant:O, is:O, derived:O, from:O, a:O, number:O, of:O, modules: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 one of these modules gathers system information and another attempts to steal the victim ’s usernames and passwords , as one would expect from a malware used for a targeted attack , the other two known OnionDuke modules are quite the opposite ; one is designed for use in attacks and the other for posting predetermined messages to the Russian VKontakte social media site .' [/INST] [While:O, one:O, of:O, these:O, modules:O, gathers:O, system:O, information:O, and:O, another:O, attempts:O, to:O, steal:O, the:O, victim:O, ’s:O, usernames:O, and:O, passwords:O, ,:O, as:O, one:O, would:O, expect:O, from:O, a:O, malware:O, used:O, for:O, a:O, targeted:O, attack:O, ,:O, the:O, other:O, two:O, known:O, OnionDuke:S-MAL, modules:O, are:O, quite:O, the:O, opposite:O, ;:O, one:O, is:O, designed:O, for:O, use:O, in:O, attacks:E-ACT, and:O, the:O, other:O, for:O, posting:O, predetermined:O, messages:O, to:O, the:O, Russian:O, VKontakte:S-TOOL, social:O, media:O, site: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 sort of functionality is more common in criminality-oriented botnets , not statesponsored targeted attacks .' [/INST] [This:O, sort:O, of:O, functionality:O, is:O, more:O, common:O, in:O, criminality-oriented:O, botnets:O, ,:O, not:O, statesponsored:O, targeted:O, attacks: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 have since been able to identify at least two separate OnionDuke botnets .' [/INST] [We:O, have:O, since:O, been:O, able:O, to:O, identify:O, at:O, least:O, two:O, separate:O, OnionDuke:S-MAL, botnets: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 formation of the first of these botnets began in January 2014 , using both unidentified infection vectors and the known malicious Tor node , and continued until our blogpost was published in November .' [/INST] [We:O, believe:O, the:O, formation:O, of:O, the:O, first:O, of:O, these:O, botnets:O, began:O, in:O, January:B-TIME, 2014:E-TIME, ,:O, using:O, both:O, unidentified:O, infection:O, vectors:O, and:O, the:O, known:O, malicious:O, Tor:S-TOOL, node:O, ,:O, and:O, continued:O, until:O, our:O, blogpost:O, was:O, published:O, in:O, November: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: 'We believe the formation of the second botnet began in August 2014 and continued until January 2015 .' [/INST] [We:O, believe:O, the:O, formation:O, of:O, the:O, second:O, botnet:O, began:O, in:O, August:B-TIME, 2014:E-TIME, and:O, continued:O, until:O, January:B-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: 'We have been unable to identify the infection vectors used for this second botnet , but the C&C servers it used had open directory listings , allowing us to retrieve files containing listings of victim IP addresses .' [/INST] [We:O, have:O, been:O, unable:O, to:O, identify:O, the:O, infection:O, vectors:O, used:O, for:O, this:O, second:O, botnet:O, ,:O, but:O, the:O, C&C:S-TOOL, servers:O, it:O, used:O, had:O, open:O, directory:O, listings:O, ,:O, allowing:O, us:O, to:O, retrieve:O, files:O, containing:O, listings:O, of:O, victim:O, IP:O, addresses: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 geographic distribution of these IP addresses ( image 8 , page 13 ) further supports our theory that the purpose of this OnionDuke variant was not targeted attacks against high-profile targets .' [/INST] [The:O, geographic:O, distribution:O, of:O, these:O, IP:O, addresses:O, (:O, image:O, 8:O, ,:O, page:O, 13:O, ):O, further:O, supports:O, our:O, theory:O, that:O, the:O, purpose:O, of:O, this:O, OnionDuke:S-MAL, variant:O, was:O, not:O, targeted:O, attacks:O, against:O, high-profile:O, targets: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 theory is that the botnets were a criminal side business for the Dukes group .' [/INST] [One:O, theory:O, is:O, that:O, the:O, botnets:O, were:O, a:O, criminal:O, side:O, business:O, for: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: 'The size of the botnet however ( about 1400 bots ) is very small if its intended use is for commercial DoS attacks or spam-sending .' [/INST] [The:O, size:O, of:O, the:O, botnet:O, however:O, (:O, about:O, 1400:O, bots:O, ):O, is:O, very:O, small:O, if:O, its:O, intended:O, use:O, is:O, for:O, commercial:O, DoS:S-TOOL, attacks:O, or:O, spam-sending: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: 'Alternatively , OnionDuke also steals user credentials from its victims , providing another potential revenue source .' [/INST] [Alternatively:O, ,:O, OnionDuke:S-MAL, also:O, steals:O, user:O, credentials:O, from:O, its:O, victims:O, ,:O, providing:O, another:O, potential:O, revenue:O, source: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 counter to that argument however is that the value of stolen credentials from users in the countries with the highest percentage of OnionDuke bots ( Mongolia and India ) are among the lowest on underground markets . 2015 : The Dukes up the ante .' [/INST] [The:O, counter:O, to:O, that:O, argument:O, however:O, is:O, that:O, the:O, value:O, of:O, stolen:O, credentials:O, from:O, users:O, in:O, the:O, countries:O, with:O, the:O, highest:O, percentage:O, of:O, OnionDuke:S-MAL, bots:O, (:O, Mongolia:S-LOC, and:O, India:S-LOC, ):O, are:O, among:O, the:O, lowest:O, on:O, underground:O, markets:O, .:O, 2015:S-TIME, ::O, The:O, Dukes:S-APT, up:O, the:O, ante: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 end of January 2015 saw the start of the most high- volume Duke campaign seen thus far , with thousands of recipients being sent spear-phishing emails that contained links to compromised websites hosting CozyDuke .' [/INST] [The:O, end:B-TIME, of:I-TIME, January:I-TIME, 2015:E-TIME, saw:O, the:O, start:O, of:O, the:O, most:O, high-:O, volume:O, Duke:S-APT, campaign:O, seen:O, thus:O, far:O, ,:O, with:O, thousands:O, of:O, recipients:O, being:O, sent:O, spear-phishing:S-ACT, emails:S-TOOL, that:O, contained:O, links:O, to:O, compromised:O, websites:O, hosting:O, CozyDuke:S-MAL, .:O] </s> |