Practical Experiences with Purenet, a Self-Learning Malware Prevention System

  • Alapan Arnab
  • Tobias Martin
  • Andrew Hutchison
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6555)


This paper introduces Purenet, which is a self-learning malware detection system aimed at avoiding zero-day attacks and other delays in patching application systems when attacks are identified. The concept and architecture of Purenet are described, specifically positioning anomaly detection as the system enabler. Deployment of the system in an operational environment is discussed, and associated recommendations and findings are presented based on this. Findings from the prototype include various considerations which should influence the design of such security software including latency considerations, multi protocol support, cloud anti-malware integration, resource requirement issues, reporting, base platform hardening and SIEM integration.


  1. 1.
    Elovici, Y., Shabtai, A., Moskovitch, R., Tahan, G., Glezer, C.: Applying machine learning techniques for detection of malicious code in network traffic. In: Hertzberg, J., Beetz, M., Englert, R. (eds.) KI 2007. LNCS (LNAI), vol. 4667, pp. 44–50. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Firstbrook, P.: Why Malware Filtering Is Necessary in the Web Gateway. Published 2008-08-26 Gartner. Gartner ID: G001584595Google Scholar
  3. 3.
    Heidari, M.: Malicious Codes in Depth (2004),
  4. 4.
    Kienzle, D.M., Elder, M.C.: Internet WORMS: Past, Present, and Future: Recent worms: a survey and trends. In: ACM Workshop on Rapid Malcode, WORM 2003 (2003)Google Scholar
  5. 5.
    Moskovitch, R., Stopel, D., Feher, C., Nissim, N., Elovici, Y.: Unknown Malcode Detection via Text Categorization and the Imbalance Problem. In: IEEE Intelligence and Security Informatics, Taiwan (2008)Google Scholar
  6. 6.
    Moskovitch, R., Feher, C., Elovici, Y.: Unknown Malcode Detection - A Chronological Evaluation. In: IEEE Intelligence and Security Informatics, Taiwan (2008)Google Scholar
  7. 7.
    Moskovitch, R., Elovici, Y.: Unknown Malicious Code Detection - Practical Issues. In: 7th European Conference on Warfare and Security, Plymouth, UK (2008)Google Scholar
  8. 8.
    Moskovitch, R., Nissim, N., Elovici, Y.: Acquisition of Malicious Code Using Active Learning. In: Bonchi, F., Ferrari, E., Jiang, W., Malin, B. (eds.) PinKDD 2008. LNCS, vol. 5456, pp. 74–91. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Symantec: Security Report (2006),
  11. 11.

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Alapan Arnab
    • 1
  • Tobias Martin
    • 2
  • Andrew Hutchison
    • 1
  1. 1.T-Systems South AfricaInternational Business GatewayMidrandSouth Africa
  2. 2.Deutsche Telekom LaboratoriesDarmstadtGermany

Personalised recommendations