Situational Awareness, Botnet and Malware Detection in the Modern Era - Davide Papini - Codemotion Milan 2016
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Looking at the modern threat landscape, it is clear that a comprehensive security infrastructure cannot rely only on traditional automatic systems such as intrusion detection, or anti-virus appliances. Considering also that more than 400K new malware are discovered every day, the need for advanced techniques to isolate and identify malware is even more evident. This talk will show an overview of modern techniques developed by security researchers and practitioners, the different perspectives and approaches they take and how they evolved to take into account malware evolution.
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Situational Awareness, Botnet and Malware Detection in the Modern Era - Davide Papini - Codemotion Milan 2016
- 1. SITUATIONAL AWARENESS, BOT- NET AND MALWARE DETECTION IN THE MODERN ERA Machine Learning Enabled Advanced Security CodeMotion Milan 2016 Davide Papini
- 2. Introduction ML for Cyber Security Final Remarks ABOUT ME Research & Innovation @Ele ronica S.p.a. Postdoc @ISG Royal Holloway, UK on ML applied to cyber situational awareness. M.Sc. Telecommunication Engineering @Politecnico di Milano: → Erasmus @Danmarks Tekniske Universitet → Master Thesis on ``Anomaly Based Wireless Intrusion Detection Systems'' Ph.D. @Danmarks Tekniske Universitet: → ``Attacker Modeling in Ubiquitous Computing Systems'' → External stay at COSIC, KU Leuven 2
- 3. Introduction ML for Cyber Security Final Remarks WHAT THIS TALK IS ABOUT Topics: Applications of ML in Cybersecurity research. Successful research: botnets, DGAs, early malware detection. ML traps. Evaluation metrics. NOT about: New ML algorithms. Showing one specific Security-ML based application. Wear you out with math. 3
- 4. Introduction ML for Cyber Security Final Remarks MOTIVATIONAL SLIDE 4
- 5. Introduction ML for Cyber Security Final Remarks MOTIVATIONAL SLIDE 4
- 6. Introduction ML for Cyber Security Final Remarks MOTIVATIONAL SLIDE 5
- 7. Introduction ML for Cyber Security Final Remarks MOTIVATIONAL SLIDE Control of the botnet for 10 days: 180,000 infections, recording of over 70GB of data. Torpig intercepts and records keystroke information at a low level, targeting a wide variety of applications and websites. Stealing financial and personal informations, login credentials for social networking etc. Torpig periodically uploads any new data that it has captured to a central server. The researchers were able to infiltrate the botnet by registering one of the domains from a list of potential ones infected machines use. 5
- 8. Introduction ML for Cyber Security Final Remarks SOME STATISTICS h ps://www.mcafee.com/us/resources/reports/rp-quarterly-threats-sep-2016.pdf 6
- 9. Introduction ML for Cyber Security Final Remarks SOME STATISTICS h ps://www.mcafee.com/us/resources/reports/rp-quarterly-threats-sep-2016.pdf 450,000 new malware per day. 20,000 is mobile malware. Includes: ransomware, botnets, rootkits, trojians … 6
- 10. Introduction ML for Cyber Security Final Remarks NEED A GAME CHANGER Modern malware/intrusions are difficult to detect/block: Code obfuscation, polimorfism and packing. Malware written ad-hoc for specific targets. AVs are mainly signature-based. URL Blacklists cannot be updated fast enough. Local changes are often too small/subtle to be detected. Logs contains lot of noise (≃ 90%) 7
- 11. Introduction ML for Cyber Security Final Remarks NEED A GAME CHANGER Modern malware/intrusions are difficult to detect/block: Code obfuscation, polimorfism and packing. Malware written ad-hoc for specific targets. AVs are mainly signature-based. URL Blacklists cannot be updated fast enough. Local changes are often too small/subtle to be detected. Logs contains lot of noise (≃ 90%) Need for intelligent approaches: Adapt to unforseen "events" Learn from data i.e. extract behaviours NOT signatures Leverage global knowledge Can be quasi-real-time. 7
- 12. ML FOR CYBER SECURITY
- 13. Introduction ML for Cyber Security Final Remarks Machine learning has been applied to many fields in security: Botnet detection and classification Mobile application analysis Spam detection and campaigns analysis Situational awareness through network traffic analysis Download malware detection and many more... Also in many flavours: Supervised Unsupervised combinations of those 9
- 14. Introduction ML for Cyber Security Final Remarks BOTNETS Situational awareness: knowledge of the health status of a network (e.g. malware infections, intrusions and data exfiltration). Botnet: a network of bots (drones), i.e. programs installed on the machines of unwitting Internet users and receiving commands from a bot controller. 10
- 15. Introduction ML for Cyber Security Final Remarks BOTNETS Situational awareness: knowledge of the health status of a network (e.g. malware infections, intrusions and data exfiltration). Botnet: a network of bots (drones), i.e. programs installed on the machines of unwitting Internet users and receiving commands from a bot controller. 10
- 16. Introduction ML for Cyber Security Final Remarks BOTNETS C&C CHANNEL Bots connect to C&C Server in three ways: Hard coded IP: Bot → 1.2.3.4 Hard coded domain: Bot → badguy.ru → 1.2.3.4 Automatically Generated Domains: → Bot cycles through time-dependent domains. → Domain names are generated using a Domain Generation Algorithm. → The botmaster needs to register only one of those domains. jhhfghf7.tk faukiijjj25.tk pvgvy.tk cvq.com epu.org bwn.org 11
- 17. Introduction ML for Cyber Security Final Remarks BOTNETS C&C CHANNEL Bots connect to C&C Server in three ways: Hard coded IP: Bot → 1.2.3.4 Hard coded domain: Bot → badguy.ru → 1.2.3.4 Automatically Generated Domains: → Bot cycles through time-dependent domains. → Domain names are generated using a Domain Generation Algorithm. → The botmaster needs to register only one of those domains. jhhfghf7.tk faukiijjj25.tk pvgvy.tk cvq.com epu.org bwn.org courtesy of E.Colombo - Cerberus 11
- 18. Introduction ML for Cyber Security Final Remarks BOTNETS C&C CHANNEL Bots connect to C&C Server in three ways: Hard coded IP: Bot → 1.2.3.4 Hard coded domain: Bot → badguy.ru → 1.2.3.4 Automatically Generated Domains: → Bot cycles through time-dependent domains. → Domain names are generated using a Domain Generation Algorithm. → The botmaster needs to register only one of those domains. jhhfghf7.tk faukiijjj25.tk pvgvy.tk cvq.com epu.org bwn.org courtesy of E.Colombo - Cerberus Sinkholing: If domain is already registered botmaster looses control of botnets! 11
- 19. Introduction ML for Cyber Security Final Remarks PHOENIX AND CERBERUS Developed at Polimi and ISG@RHUL System that relies on Machine Learning to identify DGA: Leverage known malicious and benign domain names to build a classifier: → Distinguish Human Generated Domains from AGD. → Identifies the DGA used: botnets might share the same DGA. Use unsupervised learning to identify new DGAs. Traffic comes from a na onal authoritative DNS server. S. Schiavoni et al., Phoenix: DGA-Based Botnet Tracking and Intelligence. In Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) 2014. E. Colombo, Cerberus: Detec on and Characteriza on of Automa cally-Generated Malicious Domains. Master Thesis, Politecnico di Milano 2014. 12
- 20. Introduction ML for Cyber Security Final Remarks PHOENIX AND CERBERUS Developed at Polimi and ISG@RHUL System that relies on Machine Learning to identify DGA: Leverage known malicious and benign domain names to build a classifier: → Distinguish Human Generated Domains from AGD. → Identifies the DGA used: botnets might share the same DGA. Use unsupervised learning to identify new DGAs. Traffic comes from a na onal authoritative DNS server. S. Schiavoni et al., Phoenix: DGA-Based Botnet Tracking and Intelligence. In Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) 2014. E. Colombo, Cerberus: Detec on and Characteriza on of Automa cally-Generated Malicious Domains. Master Thesis, Politecnico di Milano 2014. Malicious Domains Phoenix Clusters Time DetectiveSuspicious Domains Filtering DNS Stream Classifier Bootstrap Filtering Detection courtesy of E.Colombo - Cerberus 12
- 21. Introduction ML for Cyber Security Final Remarks CERBERUS FINDINGS 187 malicious domains detected and labeled 3,576 suspicious domains collected 47 clusters of DGA-generated domains discovered 319 new domains detected in the next 24 hours 13
- 22. Introduction ML for Cyber Security Final Remarks MASTINO: REALTIME MALWARE DETECTION Developed at TrendMicro and presented Defcon London 2016 System for advanced realtime malware detection: Leverages global knowledge on download events Classifies malware from goodware Based on statistical evidence and graph analysis: Tripartite graph: URLs, Files, Machines Intrinsic features e.g. → file: size, obfuscated, signed; → url: FQD, e2LD, query path → machine: malware download history, processes Behaviour-based features: → Consider reputation of neighboring nodes → Help to classify unknown nodes 14
- 23. Introduction ML for Cyber Security Final Remarks MASTINO: REALTIME MALWARE DETECTION Developed at TrendMicro and presented Defcon London 2016 System for advanced realtime malware detection: Leverages global knowledge on download events Classifies malware from goodware Based on statistical evidence and graph analysis: Tripartite graph: URLs, Files, Machines Intrinsic features e.g. → file: size, obfuscated, signed; → url: FQD, e2LD, query path → machine: malware download history, processes Behaviour-based features: → Consider reputation of neighboring nodes → Help to classify unknown nodes Huge work on feature enginering! 14
- 24. Introduction ML for Cyber Security Final Remarks MASTINO SYSTEM OVERVIEW Copyright 2016 Trend Micro Inc.7 System Overview courtesy of M.Balduzzi - TrendMicro 15
- 25. Introduction ML for Cyber Security Final Remarks MASTINO TRAINING AND DETECTION courtesy of M.Balduzzi - TrendMicro 16
- 26. Introduction ML for Cyber Security Final Remarks MASTINO RESULTS Mastino evaluation: On testing dataset: 95.8% TP, 0.5% FP Early detection experiment, deployed in the wild for 6 months: → Detected 84% of future malware → Verified later through VirusTotal 17
- 27. Introduction ML for Cyber Security Final Remarks MASTINO RESULTS Mastino evaluation: On testing dataset: 95.8% TP, 0.5% FP Early detection experiment, deployed in the wild for 6 months: → Detected 84% of future malware → Verified later through VirusTotal Detec on me ≃ 0.16s! 17
- 28. Introduction ML for Cyber Security Final Remarks ISSUES Traditional ML developed for ``natural'' objects: Natural Language Processing. Image analysis e.g. picture text search. Classification of plants animals. Economics laws. Metrics like ROC, FP, FN, work very well in these cases, however cyberworld is not natural: Things change abruptly e.g. updates, new malware, new technologies. There is no clear evolutionary law. Change is deterministic and unpredictable. Behaviours change/slide over time. 18
- 29. Introduction ML for Cyber Security Final Remarks ML TRAPS Machine learning often seen as a black-box panacea: Little is understood. Results with hi accuracy taken without questioning quality. However: Overfitting: if training and testing is not done carefully. Validity of results: a system that works on paper may not work in the field. Datasets: Variety vs Chronology 19
- 30. Introduction ML for Cyber Security Final Remarks ML TRAPS Machine learning often seen as a black-box panacea: Little is understood. Results with hi accuracy taken without questioning quality. However: Overfitting: if training and testing is not done carefully. Validity of results: a system that works on paper may not work in the field. Datasets: Variety vs Chronology Need for novel metrics! 19
- 31. Introduction ML for Cyber Security Final Remarks CONFORMAL EVALUATOR Library developed at Informa on Security Group at Royal Holloway: Evaluates algorithms in terms of confidence and credibility. Core is Non-Conformity measure, elicited directly from the algorithm, which in essence tells the difference between a sample and a set of samples. Builds decision and alpha assessments to evaluate the algorithm. R. Jordaney, Z. Wang, D. Papini, I. Nouretdinov and L. Cavallaro, Misleading Metrics: On Evalua ng Machine Learning for Malware with Confidence, Technical Report 2016-1 Royal Holloway University of London. 20
- 32. Introduction ML for Cyber Security Final Remarks CONFORMAL EVALUATOR Library developed at Informa on Security Group at Royal Holloway: Evaluates algorithms in terms of confidence and credibility. Core is Non-Conformity measure, elicited directly from the algorithm, which in essence tells the difference between a sample and a set of samples. Builds decision and alpha assessments to evaluate the algorithm. R. Jordaney, Z. Wang, D. Papini, I. Nouretdinov and L. Cavallaro, Misleading Metrics: On Evalua ng Machine Learning for Malware with Confidence, Technical Report 2016-1 Royal Holloway University of London. Training and Testing Dataset Similarity Based Classification/Clustering Algorithm Conformal Evaluator Alpha Assessment Decision Assessment Non-Conformity Measure Conformal Evaluator Overview 20
- 33. Introduction ML for Cyber Security Final Remarks CE: EXAMPLE 1 System for Botnet detection and classification bifrose sasfis blackenergy banbra pushdo 0.0 0.2 0.4 0.6 0.8 1.0 0.86 0.27 0.29 0.31 0.9 0.2 0.84 0.18 0.95 0.15 Average algorithm correct choice Average algorithm credibility Average algorithm confidence bifrose sasfis blackenergy banbra pushdo 0.0 0.2 0.4 0.6 0.8 1.0 0.42 0.53 0.58 0.17 0.68 0.29 0.62 0.29 0.73 0.12 Average algorithm incorrect choice Average algorithm credibility Average algorithm confidence Decision Assessment 21
- 34. Introduction ML for Cyber Security Final Remarks CE: EXAMPLE 1 System for Botnet detection and classification bifrose sasfis blackenergy banbra pushdo 0.0 0.2 0.4 0.6 0.8 1.0 0.86 0.27 0.29 0.31 0.9 0.2 0.84 0.18 0.95 0.15 Average algorithm correct choice Average algorithm credibility Average algorithm confidence bifrose sasfis blackenergy banbra pushdo 0.0 0.2 0.4 0.6 0.8 1.0 0.42 0.53 0.58 0.17 0.68 0.29 0.62 0.29 0.73 0.12 Average algorithm incorrect choice Average algorithm credibility Average algorithm confidence Decision Assessment bifrose's samples sasfis's samples blackenergy's samples banbra's samples pushdo's samples 0.0 0.2 0.4 0.6 0.8 1.0 P-values P-values: bifrose P-values: sasfis P-values: blackenergy P-values: banbra P-values: pushdo Alpha Assessment 21
- 35. Introduction ML for Cyber Security Final Remarks CE: EXAMPLE 1 System for Botnet detection and classification bifrose sasfis blackenergy banbra pushdo 0.0 0.2 0.4 0.6 0.8 1.0 0.86 0.27 0.29 0.31 0.9 0.2 0.84 0.18 0.95 0.15 Average algorithm correct choice Average algorithm credibility Average algorithm confidence bifrose sasfis blackenergy banbra pushdo 0.0 0.2 0.4 0.6 0.8 1.0 0.42 0.53 0.58 0.17 0.68 0.29 0.62 0.29 0.73 0.12 Average algorithm incorrect choice Average algorithm credibility Average algorithm confidence Decision Assessment bifrose's samples sasfis's samples blackenergy's samples banbra's samples pushdo's samples 0.0 0.2 0.4 0.6 0.8 1.0 P-values P-values: bifrose P-values: sasfis P-values: blackenergy P-values: banbra P-values: pushdo Alpha Assessment Although the algorithm has reasonably good re- sults on paper, CE shows the quality of the re- sults is not good! We run experiments on another dataset to confirm, and the classifier get worse. 21
- 36. Introduction ML for Cyber Security Final Remarks CE: EXAMPLE 2 Mobile App classification: Malware vs Goodware Correct choices Incorrect choices0.0 0.2 0.4 0.6 0.8 1.0 Average algorithm credibility for correct choice Average algorithm confidence for correct choice Average algorithm credibility for incorrect choice Average algorithm confidence for incorrect choice MALICIOUS's samples BENIGN's samples 0.0 0.2 0.4 0.6 0.8 1.0 P-values P-values: MALICIOUS P-values: BENIGN 22
- 37. FINAL REMARKS
- 38. Introduction ML for Cyber Security Final Remarks FINAL REMARKS Getting your hands in the game, what you need: You need to study a bit of ML You need a problem You need data You need good metrics In the wild analysis is a plus You need tools: → We did everything in python: Numpy, Scipy → ML libraries: sk-learn, shogun-toolbox.org 24
- 39. Introduction ML for Cyber Security Final Remarks FINAL REMARKS Machine Learning is great for Cyber Security! 25
- 40. Introduction ML for Cyber Security Final Remarks FINAL REMARKS Machine Learning is great for Cyber Security! Thanks for listening: Ques ons? 25