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

Abstract : 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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [7 references]  Display  Hide  Download

https://hal.inria.fr/hal-01581334
Contributor : Hal Ifip <>
Submitted on : Monday, September 4, 2017 - 3:07:24 PM
Last modification on : Monday, September 4, 2017 - 3:09:11 PM

File

978-3-642-19228-9_6_Chapter.pd...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Alapan Arnab, Tobias Martin, Andrew Hutchison. Practical Experiences with Purenet, a Self-Learning Malware Prevention System. 1st Open Research Problems in Network Security (iNetSec), Mar 2010, Sofia, Bulgaria. pp.56-69, ⟨10.1007/978-3-642-19228-9_6⟩. ⟨hal-01581334⟩

Share

Metrics

Record views

76

Files downloads

105