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add draft IP identifer

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Frank Xu
2023-08-15 15:30:32 -04:00
parent bd07e63c1d
commit 56fb11b324
14 changed files with 1040 additions and 232 deletions
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AI4Forensics/IP_Identifier_Fine_Tuning/IP_Identifer_Fine_Tuning.pptx Normal file
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AI4Forensics/IP_Identifier_Fine_Tuning/code/check_fine_tune_job_status.py Normal file
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import openai
openai.api_key = "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"
fine_tune_job_id = "ft-3DA3kW5k0BaHoWqwjJUB6Xwf"
# Retrieve fine-tuning job details
fine_tune_job = openai.FineTune.retrieve(fine_tune_job_id)
# Check status and other information
print("Fine-tuned Model: " + fine_tune_job.fine_tuned_model)
print("Status: " + fine_tune_job.status)
print("Job Results: ========")
print(fine_tune_job.result_files)

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AI4Forensics/IP_Identifier_Fine_Tuning/code/create_fine_tune_job.py Normal file
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import openai
openai.api_key = "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"
# check file id: print(openai.File.list())
file_id = "file-1kZdBPJtBAHOehkHuhc8z5N1"
fine_tune = openai.FineTune.create(training_file=file_id, model="ada")
# Fine tuning job id
print(fine_tune)

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IP_Extraction_Fine_Tuning/code/dataset_small.csv → AI4Forensics/IP_Identifier_Fine_Tuning/code/dataset_small.csv
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AI4Forensics/IP_Identifier_Fine_Tuning/code/dataset_test.jsonl Normal file
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735
AI4Forensics/IP_Identifier_Fine_Tuning/code/fine_tuning_steps.txt Normal file
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@@ -0,0 +1,735 @@
step,elapsed_tokens,elapsed_examples,training_loss,training_sequence_accuracy,training_token_accuracy
1,41,1,0.3422684478372903,0.0,0.0
2,82,2,0.29415758050723534,0.0,0.0
3,107,3,0.26836416228777427,0.0,0.0
4,180,4,0.09312592670424943,0.0,0.0
5,221,5,0.032864584001566985,1.0,1.0
6,254,6,0.037723244405715374,1.0,1.0
7,279,7,0.03359652970650738,1.0,1.0
8,320,8,0.030938620218075807,1.0,1.0
9,545,9,0.033140532646076915,1.0,1.0
10,570,10,0.022109474907823213,1.0,1.0
11,603,11,0.013494568530586549,1.0,1.0
12,644,12,0.028891261502634737,1.0,1.0
13,717,13,0.12056504911251574,0.0,0.0
14,758,14,0.02212080684886314,1.0,1.0
15,791,15,0.019680225299234737,1.0,1.0
16,824,16,0.02177591590909287,1.0,1.0
17,849,17,0.014783291911977964,1.0,1.0
18,882,18,0.010066903492988786,1.0,1.0
19,923,19,0.02736308410449419,1.0,1.0
20,964,20,0.023958843491738664,1.0,1.0
21,997,21,0.013991431805952742,1.0,1.0
22,1086,22,0.025196835061797172,1.0,1.0
23,1119,23,0.2619002080018254,0.0,0.0
24,1192,24,0.032936498319452064,1.0,1.0
25,1233,25,0.02153837113780901,1.0,1.0
26,1266,26,0.00555596837111807,1.0,1.0
27,1291,27,0.01250326059035918,1.0,1.0
28,1332,28,0.022308030709595188,1.0,1.0
29,1365,29,0.010133290242192743,1.0,1.0
30,1398,30,0.010532345267256459,1.0,1.0
31,1431,31,0.009011256524663622,1.0,1.0
32,1472,32,0.01997880817850819,1.0,1.0
33,1569,33,0.03683679010437724,1.0,1.0
34,1610,34,0.014420278117999261,1.0,1.0
35,1691,35,0.12407691921926019,0.0,0.0
36,1716,36,0.01208413674468223,1.0,1.0
37,1741,37,0.014551969137379878,1.0,1.0
38,1782,38,0.01770302808181441,1.0,1.0
39,1871,39,0.03599551201157738,0.0,0.0
40,1896,40,0.01572693057503784,1.0,1.0
41,1921,41,0.014244088684693752,1.0,1.0
42,1986,42,0.02648213904512886,1.0,1.0
43,2011,43,0.009828377033506211,1.0,1.0
44,2036,44,0.008748155721017004,1.0,1.0
45,2069,45,0.008366924001820736,1.0,1.0
46,2102,46,0.011377047930509433,1.0,1.0
47,2127,47,0.008601020806430219,1.0,1.0
48,2248,48,0.04921287097615035,1.0,1.0
49,2297,49,0.026384771928278497,1.0,1.0
50,2338,50,0.02090477125477628,1.0,1.0
51,2371,51,0.009900623962821556,1.0,1.0
52,2484,52,0.051265101481393294,1.0,1.0
53,2509,53,0.0036296157259736593,1.0,1.0
54,2534,54,0.00676469712406591,1.0,1.0
55,2559,55,0.00882289069411854,1.0,1.0
56,2600,56,0.021269503712057488,1.0,1.0
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58,2738,58,0.3065356110023504,0.0,0.0
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60,2804,60,0.005848990828599199,1.0,1.0
61,2829,61,0.008651581133561497,1.0,1.0
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63,2887,63,0.017904774170656307,1.0,1.0
64,2920,64,0.05931086030431288,0.0,0.0
65,2945,65,0.027005705564940093,1.0,1.0
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67,3027,67,0.02005685483966954,1.0,1.0
68,3052,68,0.0863988903636588,0.0,0.0
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70,3158,70,0.023179982355810355,1.0,1.0
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80,3656,80,0.020289044517090328,1.0,1.0
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82,3722,82,0.01820325946380035,1.0,1.0
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84,3788,84,0.017909832779812864,1.0,1.0
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88,4016,88,0.04327724820095476,1.0,1.0
89,4057,89,0.011801188715064201,1.0,1.0
90,4082,90,0.008853272037946832,1.0,1.0
91,4115,91,0.011181263769404952,1.0,1.0
92,4236,92,0.04844980792932756,1.0,1.0
93,4277,93,0.019445888512735113,1.0,1.0
94,4310,94,0.024664396458774718,1.0,1.0
95,4359,95,0.029993225713123295,1.0,1.0
96,4400,96,0.020891781284783067,1.0,1.0
97,4441,97,0.0175595372365915,1.0,1.0
98,4466,98,0.04328905451541383,0.0,0.0
99,4587,99,0.04753604759939723,1.0,1.0
100,4620,100,0.13211459680363136,0.0,0.0
101,4645,101,0.014474895536380547,1.0,1.0
102,4678,102,0.010346713354647364,1.0,1.0
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104,4744,104,0.013943990744128316,1.0,1.0
105,4777,105,0.07440461992246808,0.0,0.0
106,4802,106,0.010936432361701613,1.0,1.0
107,4827,107,0.01655654637869399,1.0,1.0
108,4860,108,0.013695586945914329,1.0,1.0
109,4901,109,0.014818748898826015,1.0,1.0
110,4942,110,0.0149494605286327,1.0,1.0
111,4975,111,0.006679666804557201,1.0,1.0
112,5088,112,0.04836043983792478,1.0,1.0
113,5177,113,0.023302274540578767,1.0,1.0
114,5210,114,0.009356137278749089,1.0,1.0
115,5235,115,0.0076565783442492825,1.0,1.0
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117,5301,117,0.19929495653994117,0.0,0.0
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734,34678,734,0.0019878166060444376,1.0,1.0

20
AI4Forensics/IP_Identifier_Fine_Tuning/code/predict_ip_single_line.py Normal file
View File

@@ -0,0 +1,20 @@
import openai
openai.api_key = "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"
FINE_TUNED_MODEL = "ada:ft-university-of-baltimore-2023-08-14-12-39-06"
# take one positive and one negative sample from dataset_test.jsonl
prompt_neg = "\t<string>3608.80.24.1.8</string>;608.80.24.1 ->"
prompt_pos = "\t<string>2.20.31.4</string>;2.20.31.4 ->"
def get_completion(prompt, max_token):
return openai.Completion.create(
model=FINE_TUNED_MODEL, max_tokens=max_token, prompt=prompt
).choices[0]["text"]
# 1 means 1 token
print(f"The truth:'Negative', the predition: '{get_completion(prompt_neg, 1)}'")
print(f"The truth:'Positive', the predition: '{get_completion(prompt_pos, 1)}'")

4
IP_Extraction_Fine_Tuning/code/preprocess_dataset.py → AI4Forensics/IP_Identifier_Fine_Tuning/code/preprocess_dataset.py
View File

@@ -13,7 +13,7 @@ json_list = []
# Iterate through each row and create the JSON format
for index, row in data.iterrows():
promote_text = (
prompt_text = (
row["text"] + ";" + row["ip"] + " ->"
) # Replace with the actual column name for "promote" text
completion_text = (
@@ -21,7 +21,7 @@ for index, row in data.iterrows():
) # Replace with the actual column name for "completion" text
# Create a dictionary for the JSON object
json_object = {"promote": promote_text, "completion": completion_text}
json_object = {"prompt": prompt_text, "completion": completion_text}
json_list.append(json_object)

13
AI4Forensics/IP_Identifier_Fine_Tuning/code/upload_dataset_train.py Normal file
View File

@@ -0,0 +1,13 @@
import os
import openai
openai.api_key = "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"
# Path to training data
training_dataset = "./openAI/dataset_train.jsonl"
# Upload file to openai
upload = openai.File.create(file=open(training_dataset, "rb"), purpose="fine-tune")
# Get file id
print(upload["id"])

14
AI4Forensics/IP_Identifier_Fine_Tuning/code/view_fine_tuning_steps.py Normal file
View File

@@ -0,0 +1,14 @@
import openai
openai.api_key = "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"
# Returns the contents of the specified file
content = openai.File.download("file-ngHRotibSy82Mr55IjsPP2uK")
# Save content to a local file
file_name = "./openAI/fine_tune_steps.txt" # Change to the desired file name
with open(file_name, "wb") as f:
f.write(content)
print(f"File '{file_name}' saved successfully.")

46
IP_Extraction_Fine_Tuning/code/dataset_test.jsonl
View File

@@ -1,46 +0,0 @@
{"promote": "\t<string>3608.80.24.1.8</string>;608.80.24.1 ->", "completion": " negative"}
{"promote": "\t<string>2.20.31.4</string>;2.20.31.4 ->", "completion": " positive"}
{"promote": "\t\t<string>13.3.1.17D50</string>;13.3.1.17 ->", "completion": " positive"}
{"promote": "\t\t<string>13.3.1.17D50</string>;13.3.1.17 ->", "completion": " positive"}
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{"promote": "\t<string>257.0.1.38.116</string>;257.0.1.38 ->", "completion": " negative"}
{"promote": "\t<string>257.0.1.38.116</string>;257.0.1.38 ->", "completion": " negative"}
{"promote": "\t\t\t<string>tincan_register_device_256.0.1.26.113</string>;256.0.1.26 ->", "completion": " negative"}
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{"promote": "\t<string>3608.80.24.1.8</string>;608.80.24.1 ->", "completion": " negative"}
{"promote": "\t\t\t\t\t\t<string>255.255.255.255</string>;255.255.255.255 ->", "completion": " positive"}
{"promote": "\t\t\t\t\t\t<string>0.0.0.0</string>;0.0.0.0 ->", "completion": " negative"}
{"promote": "\t\t\t\t\t\t<string>255.255.255.255</string>;255.255.255.255 ->", "completion": " positive"}
{"promote": "\t\t\t\t\t\t<string>0.0.0.0</string>;0.0.0.0 ->", "completion": " negative"}

183
IP_Extraction_Fine_Tuning/code/dataset_train.jsonl
View File

@@ -1,183 +0,0 @@
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4
README.md
View File

@@ -35,7 +35,7 @@ or in BibTeX
---
## Table of Contents (Improve Morris Worm Attack and Email Harassment with tshark investigation on 2/2023)
## Table of Contents (Add the draft (in progress): AI for Forensics - Identifying IPs with a Fine-tuned Model, 8/2023)
- Basic Computer Skills for Digital Forensics
@@ -81,6 +81,8 @@ or in BibTeX
- Forensic Intelligence Repository
- [Email forensics](/STIX_for_digital_forensics/Email_Harassment)
- [Illegal Possession of Images](/STIX_for_digital_forensics/Illegal_Possession_Images)
- AI for Forensics
- [Identifying IP Addresses using a Fine-tuned AI Model](/AI4Forensics/IP_Identifier_Fine_Tuning/IP_Identifer_Fine_Tuning.pptx)
## Tool Installation (newly added on 12/6/2021)