Exploring Dark Networks: From the Surface Web to the Dark Web
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1 Exploring Dark Networks: From the Surface Web to the Dark Web Hsinchun Chen, Ph.D., University of Arizona Panel 3: Multi-Level, High-Dimensional Evolving and Emerging Networks (ML- HD-EEN) October 12, 2017 Leveraging Advances in Social Network Thinking for National Security: A Workshop Acknowledgements: NSF, DHS, DOJ, DOD
2 My Background Academic: Data/computational scientist; data/text/web mining, visualization; applied AI for security and health analytics Applications: Security analytics; dark networks Projects: COPLINK ( , gang/narcotic networks); Dark Web (2001-present, extremist/terrorist networks); Hacker Web (2009- present) SBE collaborators: M. Sageman, R. Breiger, T. Holt Agency collaborators: TPD, PPD, FBI, CIA, NSA, DHS (NSF, DOD)
3 Springer, 2006 Springer, 2012
4 A Vehicle to Watch via its Networks? Shape Indicates Object Type circles are people rectangles are vehicles Color Denotes Activity History Gang related Violent crimes Narcotics crimes Violent & Narcotics Larger Size Indicates higher levels of activity Border Crossing Plates are outlined in Red 8
5 Time of Day Nov 11 Nov 17 Dec 19 Dec 21 Dec 29 Jan 6 Jan 6 Jan 6 Jan 15 Vehicle A Vehicle B Jan 19 Jan 26 < 2004 Dates 2005 > Jan 31 Feb 27 Mar 5 Mar 5 May 18 May 18 May 25 May 28 May 30 Jun 9 June 17 COPLINK Identity Resolution and Criminal Network Analysis (DHS) Cross-jurisdictional Information Sharing/Collaboration Arizona IDMatcher Law-enforcement Data CAN Visualizer AZ CA TX Border Crossing Data (AZ, CA, TX) Vehicles People Identity Resolution Criminal Network Analysis Identity Match High-risk Vehicle Identification Criminal Link Prediction Suspect Traffic Burst Detection Name Match DOB Match Address Match ID Match Law-enforcement Data Border Crossing Data Narcotics Network Mutual Information First Name Match Middle Name Match Last Name Match DOB Similarity Address Similarity ID Similarity First Name Similarity Middle Name Similarity Last Name Similarity Detect false and deceptive identities across jurisdictions using a probabilistic naïve- Bayes based resolution system. Vehicle A Frequent Crossers at Night Vehicle B Identify high-risk vehicles using association techniques like mutual information using border crossing and law enforcement data. Predict interaction between individuals and vehicles using link prediction techniques to identify high-risk border crossers. Detect real-time anomalies and threats in border traffic using Markov switching and other models. * Only the grayed datasets are available to the AI Lab 9
6 Dark Web Overview Dark Web: Terrorists and cyber criminals use of the Internet Collection: Web sites, forums, blogs, YouTube, etc. 20 TBs in size, with close to 10B pages/files/messages (the entire LOC collection: 15 TBs)
7 Dark Web Forum Crawler System: Probing the Hidden Web (Proxy, TOR)
8 CyberGate for Social Media Analytics: Ideational, Textual and Interpersonal Information 12
9 Arabic Writeprint Feature Set: Online Authorship Analysis Feature Set (418) Lexical Syntactic Structural (79) Content Specific (262) (62) (15) Violence Race/Nationality Technical Structure Word Structure Word Roots Function Words Punctuation Word-Based Char-Based (48) (31) (12) (200) (50) (14) (48) (11) (4) Hyperlinks Embedded Images Font Size Font Color Contact Information Paragraph Level Message Level Elongation Word Length Dist. Vocab. Richness Word-Level Special Char. Letter Frequency Char-Level (4) (35) (9) (6) (8) (15) (2) (5) (6) (3) (29) (8) (4) (7)
10 Arabic Feature Extraction Component 1 Incoming Message 2 Elongation Filter Count +1 Degree + 5 Filtered Message Feature Set Root Dictionary 3 Root Clustering Algorithm Similarity Scores (SC) max(sc)+1 All Remaining Features Values Generic Feature Extractor 4
11 CyberGate System Design: Writeprints 15
12 Author Writeprints Author A Anonymous Messages 10 messages Author B 10 messages
13 AZ Forum Portal 13M messages (340K members) across 29 major Jihadi forums in English, Arabic, French, German and Russian (VBulletin) Linking members over time
14 18
15 Hacker Assets Portal V2.0 Overview (a) Home page, linking to (b & c) Assets, (d) Dashboard, and (e) Malware Families: (e) Malware Families, for depicting relationships among assets over time (Crypter Family shown) (b) Assets page, linking to Source Code and Attachments (c) Source Code page; sortable by asset name, exploit type, date, etc. (d) Dashboard for drill-down analysis of hackers & assets over time 20
16 Cyber Threat Intelligence (CTI) Example Bank Exploits Filtering on 2014, when BlackPOS was posted, shows assets and threat actors at that time. 2. Filtering the actor who posted BlackPOS reveals that he posts other bank exploits (e.g., Zeus). Provides intelligence on which hacker to monitor. 21
17 Cyber Threat Intelligence (CTI) Example Crypters Filtering on a specific time point (highest peak): 2. Filtering on a specific asset (crypters, a key technology for Ransomware) 3. Filtering a specific crypter author (Cracksman) shows the trends and types of assets he posted. 22
18 Selected Challenges for ML-HD-EEN in Dark Networks Identifying data Sources: availability (data stovepipes, data integration; RMS, RDBMS); web OSINT (surface web, deep web, dark web; TOR, ICT); data types (structured vs. unstructured; multi-lingual, multi-media; source code, attachment, tutorial), data biases (noise, deception, adversarial; vigilante, honeypot, APTs) Recognizing nodes: levels/dimensions (who/what/where/when/why/how); entity extraction and recognition (identity resolution, web authorship analysis, writeprint) Establishing links: linked by associations (labeled links, probabilistic links); linked by time/space (same-time-same-place; border crossing, hotspot); linked by conversations (linguistic cues and styles; ICT, forums) Analyzing network patterns: (many SNA techniques) Tracking changes over time: stream data collection & mining (update & alert; anomaly detection, concept drift; emerging cyber threats and DarkNet Markets
19 Selected Solutions & Directions for ML-HD EEN Comprehensive & timely OSINT data collection: from the surface web to the dark web; across level/dimension, over time Data integration and SNA extraction: AI assisted entity/relationship recognition/integration; across level/dimension, over time Methodological foundations: dark networks, hidden networks; noise, deception, adversarial intent Data analytics: advanced social media analytics, stream data mining, adversarial machine learning, BIG DATA analytics; across level/dimension, over time
20 For questions and comments
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