Ariu - Workshop on Artificial Intelligence and Security - 2011

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Ariu - Workshop on Artificial Intelligence and Security - 2011

  1. 1. PRA Pattern Recognition and Applications Group Machine Learning in Computer Forensics (and the Lessons Learned from Machine Learning in Computer Security) D. Ariu G. Giacinto F. Roli AISEC 4° Workshop on Artificial Intelligence and Security Chicago – October 21, 2011 Pattern Recognition and Applications GroupP R ADepartment of Electrical and Electronic Engineering University of Cagliari, Italy
  2. 2. What can be analyzed… (during an investigation)October 21 - 2011 Davide Ariu - AISEC 2011 2
  3. 3. Role of Computer Forensics (with respect to Computer Security)Prevention Detection Truth Assessment Security Security Forensics (live) Forensics Cyber Attack (or Crime) Progress October 21 - 2011 Davide Ariu - AISEC 2011 3
  4. 4. Goals• To provide a small snapshot of ML research applied to Computer Forensics• To clarify the ML approach to Computer ForensicsOctober 21 - 2011 Davide Ariu - AISEC 2011 4
  5. 5. Historical Perspective Computer Security Computer Forensics•Early ’70s – First Computer Security •1984 – The FBI Laboratory beganresearch research papers appear developing programs to examine computer evidence•1988 - The first known internet- •1993 – International Lawwide attack occur (the “Morris Enforcement Conference onWorm”) Computer Evidence •1999-2007 – Computer Forensics•Early 2000 - Slammer and his friend “Golden Age” [Garfinkel,2010]in the wild: consequent securityissues are on tv channels andnewspapers October 21 - 2011 Davide Ariu - AISEC 2011 5
  6. 6. Computer Security Research• Strong Research Community – Research groups and centers exist (almost) worldwide• Well defined main research directions – Malware and Botnet analysis and detection – Web Applications Security – Intrusion Detection – Cloud Computing• Well defined methodologies – Research results can have an immediate practical impactOctober 21 - 2011 Davide Ariu - AISEC 2011 6
  7. 7. Computer Forensics Research• Not particularly strong research community (at least in terms of results achieved) – Mostly people with a computer security background (as me..)• Not well defined research directions• Not well defined approaches and methods – Difficulty to reproduce digital forensics research results [Garfinkel, 2009]October 21 - 2011 Davide Ariu - AISEC 2011 7
  8. 8. How can machine learning be useful in Computer Forensics?• “Machine Learning methods are the best methods in applications that are too complex for people to manually design the algorithm” [Mitchell,2006]• The “reasoning” is a fundamental step during the investigation – Computer forensics is conceptually different from Intrusion Detection• The huge mass of data to be analyzed (TB scale) makes intelligent analysis methods necessary – Situations also exist where there is no time for an in- depth analysis (e.g. Battlefield Forensics)October 21 - 2011 Davide Ariu - AISEC 2011 8
  9. 9. ML applications to CF• Applications of Machine Learning techniques have been proposed in several Computer Forensics applications – Textual Documents and E-mail forensics – Network Forensics – Events and System Data Analysis – Automatic file (fragment) classificationOctober 21 - 2011 Davide Ariu - AISEC 2011 9
  10. 10. Computer Forensics Research Drawbacks• The experimental results proposed are not completely convincing… – Network forensics solutions evaluated on the DARPA dataset only – Email forensics algorithms evaluated on a corpus of 156 emails (and 3 different authors) – Automatic File classification algorithms evaluated on 500MB dataset (best case…)• In addition, the approach adopted was the same adopted in Computer Security…October 21 - 2011 Davide Ariu - AISEC 2011 10
  11. 11. How to improve existing tools?• Useful solutions can be developed only if the focus is: – On the investigator and on the knowledge of the case that he has – On the organizazion and categorization of of the information provided to the investigator • Data sorting and categorization • Prioritisation of results[Garfinkel, 2010; Beebe, 2009]October 21 - 2011 Davide Ariu - AISEC 2011 11
  12. 12. Putting knowledge into the tool…• Computer Security tools (e.g. IDS) are based on a well defined criteria that is used to detect attacks• In other contexts where is difficult to explicitely define a search criteria the feedback provided by the user is exploited to achieve more accurate results – E.g. Content-based Image Retrieval with relevance feedback [Zhouand,2003]• It can be definitely the case of Computer Forensics applications..October 21 - 2011 Davide Ariu - AISEC 2011 12
  13. 13. Organizing data and results• Discerning among the huge mass of data represent a dramatically time-consuming task for investigators – E.g. Filtering the results obtained after file carving – E.g. Inspecting all the pictures found in a laptop• A tool can be definitely useful even if it is only able to sort results and contents according to a relevance criteria (most relevant first) – The tool only assign “scores”, the analyst will inspect them..October 21 - 2011 Davide Ariu - AISEC 2011 13
  14. 14. To summarize..• We investigated the problem of applying ML to Computer Forensics• We provided a short overview of the literature related to ML applications in Computer Forensics• We proposed several guidelines to profitably apply machine learning to Computer ForensicsOctober 21 - 2011 Davide Ariu - AISEC 2011 14
  15. 15. Question or Comments Thank you for your attention! davide.ariu@diee.unica.itOctober 21 - 2011 Davide Ariu - AISEC 2011 15

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