Reduction of False Positive Intrusions by using Neural Nets

Paper Reduction of False Positive Intrusions by using Neural Nets, which I worked on with colleagues, is now available at IEEE Digital Library.

Abstract

The main idea of this paper is to propose a new solution for a Wireless Intrusion Detection Prevention System (WIDPS). The proposed WIDPS has a high degree of autonomy in tracking suspicious activity and detecting positive intrusions. Our focus was the reduction of detected false positive intrusion by implementing adaptive self-learning neural net in the system. Once it is fully developed and tested, this WIDPS would enable real-time response against threats, even to zero-day attacks.

Remark: Subscription to IEEE Digital Library required to download full paper in PDF format.

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About Dragan Pleskonjic

Chief Security Officer (CSO), Adjunct Professor, Security Researcher, Entrepreneur, Security Architect & Adviser, Software Development Manager, ISO 27001 Certified ISMS Lead Auditor, PCI ISA (Payment Card Industry Internal Security Assessor) More info about Dragan Pleskonjic.
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3 Responses to Reduction of False Positive Intrusions by using Neural Nets

  1. Pingback: all books » Blog Archive » Reduction of False Positive Intrusions by using Neural Nets

  2. Any chance of posting it publicly (or at least emailing it to me :-)) I am really curious to see it…

  3. I have to see details of IEEE Copyright form and find out what I am allowed to do before I do public posting or anything.

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