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Predictive Blacklisting as an Implicit Recommendation System

Predictive Blacklisting as an Implicit Recommendation System. Authors: Fabio Soldo, Anh Le, Athina Markopoulou IEEE INFOCOM 2010 Reporter: Jing Chiu Advisor: Yuh-Jye Lee Email: D9815013@mail.ntust.edu.tw. Outlines. Introduction Blacklists Recommendation System Related Works LWOL GWOL

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Predictive Blacklisting as an Implicit Recommendation System

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  1. Predictive Blacklisting as an Implicit Recommendation System Authors: Fabio Soldo, Anh Le, Athina Markopoulou IEEE INFOCOM 2010 Reporter: Jing Chiu Advisor: Yuh-Jye Lee Email: D9815013@mail.ntust.edu.tw Data Mining & Machine Learning Lab

  2. Outlines • Introduction • Blacklists • Recommendation System • Related Works • LWOL • GWOL • HPB • Room for improvement • DSHIELD Dataset Observation • Model Overview • Time Series • EWMA • Neighborhood Model • kNN • CA • Evaluation • Conclusions Data Mining & Machine Learning Lab

  3. Introduction • Blacklists • Recommendation System Data Mining & Machine Learning Lab

  4. Related Works • Local Worst Offender List(LWOL) • Global Worst Offender List(GWOL) • Highly Predictive Blacklisting(HPB) • J. Zhang, P. Porras, and J. Ullrich, “Highly predictive blacklisting,” in Proc. of USENIX Security ’08 (Best Paper award), San Jose, CA, USA, Jul. 2008, pp. 107–122. Data Mining & Machine Learning Lab

  5. Room for improvement Data Mining & Machine Learning Lab

  6. DSHIELD Dataset Observation Data Mining & Machine Learning Lab

  7. DSHIELD Dataset Observation(cont.) Data Mining & Machine Learning Lab

  8. DSHIELD Dataset Observation(cont.) Data Mining & Machine Learning Lab

  9. Model Overview • Time Series for Attack Prediction • Exponential Weighted Moving Average(EWMA) • Neighborhood Model • Victim Neighborhood (kNN) • k-nearest neighbor • Pearson correlation as similarity metric • Joint Attacker-Victim Neighborhood (CA) • cross-associations • Fully automatic clustering algorithm that finds row and column groups of sparce binary matrices Data Mining & Machine Learning Lab

  10. Evaluations • Local approaches • Global (neighborhood) approaches • Proposed combined method • Robustness Data Mining & Machine Learning Lab

  11. Evaluations (cont.) Data Mining & Machine Learning Lab

  12. Evaluations (cont.) Data Mining & Machine Learning Lab

  13. Evaluations (cont.) Data Mining & Machine Learning Lab

  14. Evaluations (cont.) Data Mining & Machine Learning Lab

  15. Conclusions • Frame the problem as an implicit recommendation system • Analyze a real dataset of 1-month logs from Dshield.rg • Shows that even larger improvement can be obtained • Give a methodological development with improvement over state-of-the-art. Data Mining & Machine Learning Lab

  16. Thanks for your attention • Questions? Data Mining & Machine Learning Lab

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