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import os
import openai
import json
import rdflib
class ExampleGenerator:
def __init__(self):
self.ontologies = {}
self.ontology_files = []
self.rules = {}
def add_ontology(self, onto):
if onto in self.ontology_files:
raise ValueError("Ontology file already exists.")
else:
onto_data = self.get_ontology_file(onto)
if onto_data:
self.ontology_files.append(onto)
self.ontologies[onto] = self.get_ontology_file(onto)
self.rules[onto] = self.generate_rules(onto)
else:
raise ValueError("Ontology file error.")
def get_ontology_file(self,filename):
text = ""
if os.path.isfile(filename):
with open(filename,'r') as f:
text = f.read()
f.close()
return text
else:
raise ValueError("Invalid filename.")
def ChatGPTTextSplitter(self,text):
"""Splits text in smaller subblocks to feed to the LLM"""
prompt = f"""The total length of content that I want to send you is too large to send in only one piece.
For sending you that content, I will follow this rule:
[START PART 1/10]
this is the content of the part 1 out of 10 in total
[END PART 1/10]
Then you just answer: "Instructions Sent."
And when I tell you "ALL PARTS SENT", then you can continue processing the data and answering my requests.
"""
if type(text) == str:
textsize = 12000
blocksize = int(len(text) / textsize)
if blocksize > 0:
yield prompt
for b in range(1,blocksize+1):
if b < blocksize+1:
prompt = f"""Do not answer yet. This is just another part of the text I want to send you. Just receive and acknowledge as "Part {b}/{blocksize} received" and wait for the next part.
[START PART {b}/{blocksize}]
{text[(b-1)*textsize:b*textsize]}
[END PART {b}/{blocksize}]
Remember not answering yet. Just acknowledge you received this part with the message "Part {b}/{blocksize} received" and wait for the next part.
"""
yield prompt
else:
prompt = f"""
[START PART {b}/{blocksize}]
{text[(b-1)*textsize:b*textsize]}
[END PART {b}/{blocksize}]
ALL PARTS SENT. Now you can continue processing the request.
"""
yield prompt
else:
yield text
elif type(text) == list:
yield prompt
for n,block in enumerate(text):
if n+1 < len(text):
prompt = f"""Do not answer yet. This is just another part of the text I want to send you. Just receive and acknowledge as "Part {n+1}/{len(text)} received" and wait for the next part.
[START PART {n+1}/{len(text)}]
{text[n]}
[END PART {n+1}/{len(text)}]
Remember not answering yet. Just acknowledge you received this part with the message "Part {n+1}/{len(text)} received" and wait for the next part.
"""
yield prompt
else:
prompt = f"""
[START PART {n+1}/{len(text)}]
{text[n]}
[END PART {n+1}/{len(text)}]
ALL PARTS SENT. Now you can continue processing the request.
"""
yield prompt
def send_ontology(self):
ontology = ""
if len(self.ontologies) > 0:
for k,v in self.ontologies.items():
ontology+=v+"\n"
print("Sending Ontology in Parts")
for i in self.ChatGPTTextSplitter(ontology):
print(self.llm_api(i))
else:
raise ValueError("No loaded ontology to send.")
def llm_api(self,prompt,model="gpt-3.5-turbo"):
messages = [{
"role":"user",
"content":prompt
}]
res = openai.ChatCompletion.create(model=model,messages=messages,temperature=0)
return res.choices[0].message['content']
def generate_rule(self,onto=None):
"""Raw rule string of AEO."""
v = """Remember make a json-ld format example that only uses classes and properties terms from Adversary Engagement Ontology, Unified Cyber Ontology.
Each engagement:Narrative has property:
engagement:hasStoryline connects to an engagement:Storyline
Each engagement:Storyline has property:
engagement:hasEvent connects to a uco-types:Thread
Each uco-types:Thread has properties:
co:element contains all engagement:PlannedEvents
co:item contains all uco-types:ThreadItem one each for each engagement:PlannedEvent.
co:size
uco-types:threadOriginItem is the uco-types:ThreadItem for the first engagement:PlannedEvent
uco-types:threadTerminalItem is the uco-types:ThreadItem for the last engagement:PlannedEvent
Each co:size has properties:
@type as xsd:nonNegativeInteger
@value which is the number of uco-types:ThreadItem
Each uco-types:ThreadItem has property:
co:itemContent is the engagement:PlannedEvent
optional uco-types:threadNextItem is the next uco-types:ThreadItem for the next engagement:PlannedEvent if there is one,
optional uco-types:threadPreviousItem is the previous uco-types:ThreadItem for the previous engagement:PlannedEvent if there is one
Each engagement:PlannedEvent has property:
engagement:eventContext connects to one engagement action has property @type one of the following:
engagement:Access
engagement:Alert
engagement:Beacon
engagement:Deploy
engagement:Obfuscate
engagement:Respond
Each engagement action has properties:
@type is the action
uco-core:performer
uco-core:object connects to one of the following engagement deception object denoted as "EDO" objects:
engagement:Honeypot
engagement:Honeytoken
engagement:Breadcrumb
engagement:BreadcrumbTrail
engagement:LureObject
engagement:HoneyObject
engagement:Decoy
engagement:DataSource
Each "EDO" object has properties:
engagement:hasCharacterization connects to a uco-core:UcoObject
objective:hasObjective with @type objective:Objective and @id with one of the following instances:
objective:CommandAndControl
objective:CredentialAccess
objective:DevelopResource
objective:Discover
objective:EscalatePrivilege
objective:Evade
objective:Execute
objective:Exfilitrate
objective:GainInitialAccess
objective:Impact
objective:MoveLaterally
objective:Persist
objective:Reconnaissance
objective:Affect
objective:Collect
objective:Detect
objective:Direct
objective:Disrupt
objective:Elicit
objective:Expose
objective:Motivate
objective:Plan
objective:Prepare
objective:Prevent
objective:Reassure
objective:Analyze
objective:Deny
objective:ElicitBehavior
objective:Lure
objective:TimeSink
objective:Track
objective:Trap
uco-core:name is the objective
All people have property:
@type is uco-identity:Person
uco-core:hasFacet that connects to one of the following:
uco-identity:SimpleNameFacet which has the property:
uco-identity:familyName
uco-identity:givenName
Each uco-core:Role has properties:
@id is the role
uco-core:name is the role
Each uco-core:Role there is a uco-core:Relationship with properties:
uco-core:kindofRelationship is "has_Role"
uco-core:source connects to the person who has the role
uco-core:target connects to uco-core:Role
Each engagement:BreadcrumbTrail has property:
engagement:hasBreadcrumb connects to uco-types:Thread
This uco-types:Thread has property:
co:element contains all engagement:Breadcrumb that belong to this engagement:BreadcrumbTrail
co:item contains all uco-types:ThreadItem one each for each engagement:Breadcrumb
co:size
uco-types:threadOriginItem is the uco-types:ThreadItem for the first engagement:Breadcrumb belonging to this engagement:BreadcrumbTrail
uco-types:threadTerminalItem is the uco-types:ThreadItem for the last engagement:Breadcrumb belonging to this engagement:BreadcrumbTrail
Each engagement:Breadcrumb has the properties:
engagement:hasCharacterization which connects to a uco-core:UcoObject with the property:
uco-core:description which describes the object characterizing the breadcrumb
All classes must include property:
@type is the class
@id is a unique identifier
If namespace "engagement" prefix is used then https://ontology.adversaryengagement.org/ae/engagement#
If namespace "objective" prefix is used then https://ontology.adversaryengagement.org/ae/objective#
If namespace "role" prefix is used then https://ontology.adversaryengagement.org/ae/role#
If namespace "identity" prefix is used then https://ontology.adversaryengagement.org/ae/identity#
If namespace "uco-core" prefix is used then https://ontology.unifiedcyberontology.org/uco/core#
If namespace "uco-types" prefix is used then https://ontology.unifiedcyberontology.org/uco/types#
If namespace "uco-role" prefix is used then https://ontology.unifiedcyberontology.org/uco/role#
"""
return v
def generate_continue(self):
v = """
continue
"""
return v
def raw_prompt(self,description):
def run(val):
prompt = f"""Give me a full json-ld format example for the following scenario:
{description}
{"".join(val)}
"""
for i in self.ChatGPTTextSplitter(prompt):
res = self.llm_api(i)
return res
# return json.loads(res)
res_val = run(self.generate_rule())
#res_val = run(self.generate_rules())
try:
val = json.loads(res_val)
return val
except:
#the response was cut off, prompt for the continuation.
data = []
data.append(res_val)
while True:
res = self.llm_api(self.generate_continue())
data.append(res)
try:
full = "".join(data)
return json.loads(full)
except:
pass
return None
def get_ns(self,string):
return string.split(":")[0]
def prompt(self,description):
res = self.raw_prompt(description)
#include only relevent namespaces
prefixes = []
def is_nested(LIST):
if type(LIST) == list:
for JSON in LIST:
for key in JSON.keys():
if type(JSON[key]) == dict:
is_nested(JSON[key])
if '@type' in JSON.keys():
prefixes.append(self.get_ns(JSON['@type']))
else:
JSON = LIST
for key in JSON.keys():
if type(JSON[key]) == dict:
is_nested(JSON[key])
if '@type' in JSON.keys():
prefixes.append(self.get_ns(JSON['@type']))
is_nested(res['@graph'])
prefixes = set(prefixes)
new_prefixes = {}
for prefix in prefixes:
if prefix in res['@context']:
new_prefixes[prefix] = res['@context'][prefix]
res['@context'] = new_prefixes
return res
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