AI and Machine Learning for Analyzing Cryptocurrencies and the Blockchain
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1) CipherTrace provides intelligence, compliance, forensics and security services for cryptocurrencies, crypto assets and blockchains to make them safe and trusted. 2) They use machine learning algorithms and proprietary intelligence networks to trace criminal activity like ransomware, money laundering, and terrorist funding on blockchains and cryptocurrency exchanges. 3) By tracing transactions, CipherTrace can identify exchanges, money transmitters, and other businesses that may need enhanced anti-money laundering controls to comply with regulations.
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AI and Machine Learning for Analyzing Cryptocurrencies and the Blockchain
- 1. AI and Machine Learning for Analyzing Cryptocurrencies and the Blockchain Making cryptocurrencies, crypto assets and blockchains safe and trusted November, 2019 | Dave Jevans, CEO, CipherTrace
- 2. CipherTrace WHO WE ARE Global intelligence, compliance, forensics & security for cryptocurrencies, crypto assets and blockchains.
- 3. Proprietary and Confidential 3 Cryptocurrency Intelligence Solutions CipherTrace Anti-money laundering Financial investigations Regulatory supervision Bank risk intelligence Market intelligence
- 4. Global Customers: Banking, Crypto, Government 4Proprietary and Confidential
- 5. 5Proprietary and Confidential 1,600 Cryptocurrencies Use Blockchain
- 6. CipherTrace Intelligence Overlay Network Across Blockchains and Currencies 6 HOW IT WORKS Global Moneypots Network Financial Intel Accounts Dark Market / Deep Web Full Blockchain Nodes Mempool Realtime Data Verified User Feedback Private Threat Intelligence APWG eCrime Exchange Stolen Cryptocurrencies Fraudulent ICOs Ransomware Analysis Malware Analysis Honey Pots Risk-Scored Transactions and Wallets1.5 – 2 million new attributions per week A C C E S S A C T I O N A B L E I N T E L L I G E N C E P R O P R I E T A R Y M A C H I N E L E A R N I N G A L G O R I T H M S O V E R L A Y I N T E L L I G E N C E N E T W O R K Rich UI API Bayesian Multi-input Clustering Inductive Logic Learning Classifier Reinforcement Decision Tree Association Similarity IntelNodesLabsNetwork Criminal Dark Market Terrorist Ransomware Stolen Token Fraudulent ICO Gambling Mixer ATM Exchange Proprietary and Confidential
- 7. 7Proprietary and Confidential How to get Training Datasets and Labels
- 9. Proprietary and Confidential 9 [EnExchanger | 2019 Nov | http://www.enexchanger.com]
- 10. Proprietary and Confidential 10 [Coin ATM Rader | 2018 Sep | https://coinatmradar.com/]
- 11. Tracing Ransomware 11 W A N N A C R Y P E T Y A / N O T P E T Y A S A M S A M B A D R O B O T J A F F R A N S O M W A R E G A N D C R A B Spread Vector: Vulnerability in SMB Protocol (MS17– 010 ) Decryptor: https://github.com/gentilkiwi/wanakiwi/releases Data about exchanges around the world for integration into your current AML and compliance systems Criminal threats and activity Entity monitoring Spread Vector: ETERNALBLUE/ETERNALROMANCE SMBv1 -Uses harvested credentials from victims to log into SMB shares Decryptor: https://blog.malwarebytes.com/malwarebytes- news/2017/07/bye-bye-petya-decryptor-old-versions- released/ Spread Vector: drive-by from websites. Pretends to be Adobe Flash installer, requiring manual installation -EternalRomance exploit as an infection vector to spread within corporate networks No decryptor available yet Spread Vector: web-facing services such as SMB, RDP, VNC and JBoss are likely the initial access vector to the network No Decryption Tool Available Yet. Spread Vectors: Javascript and Doc downloaders attached to e-mails -Drive-by download using exploit kits Decryptor Tools are available based on which version is infecting. Spread Vector: Necurs Botnet using malicious PDF files that have an embedded docm file, which in its turn downloaded an encoded executable. -Same delivery as Locky, Dridex and Bart are distributed Decryptor: https://www.nomoreransom.org/en/decryption-tools.html Proprietary and Confidential
- 12. 12Proprietary and Confidential Tracing Money Laundering [BITCOIN MIXER | 2019 Nov | https://bitcoinmix.org/]
- 13. Identifying Money Laundering Mixers
- 14. [Benjamin Strick at Medium | 2019 Nov | https://medium.com/@benjamindbrown/tracing-syrian-cell- kidnappers-scammers-finances-through-blockchain-e9c52fb6127d]
- 17. 17
- 18. Using Machine Learning to Trace Ransomware • Example: Locky Ransomware • We encrypt a virtual device and pay off Locky ransomware
- 19. Tracing Locky to BTC-e.com
- 20. Tracing Addresses At Ransomware Exchangers
- 21. Tracing Thousands of Payoffs Finds These Ransomware Exchangers
- 22. • Automates compliance • Risk scores transactions • Enhanced due diligence Cryptocurrency Anti-Money Laundering (AML) Exchanges, Funds and MSBs
- 23. Training the Model to Identify Large Scale Money Businesses 23
- 24. ML Finds Transaction Processors 24
- 28. Cryptocurrency Exchange With and Without AML Controls 28 EFFECTIVE AML CONTROLS W I T H A M L C O N T R O L S W I T H O U T A M L C O N T R O L S Proprietary and Confidential
- 29. 29Proprietary and Confidential Cryptocurrency AML, Security & Intelligence OUR CAPABILITIES Financial & Threat Intelligence Cryptocurrency Anti-Money Laundering (Crypto AML) Financial Investigations Regulatory and Audit