Paper Info
Title
Detecting Anomalous Process Behaviour Using Second Generation Artificial Immune Systems
Abstract
Artificial Immune Systems have been successfully applied to a number of problem domains including fault tolerance and data mining, but have been shown to scale poorly when applied to computer intrusion detection despite the fact that the biological immune system is a very effective anomaly detector. This may be because AIS algorithms have previously been based on the adaptive immune system and biologically-naive models. This paper focuses on describing and testing a more complex and biologically-authentic AIS model, inspired by the interactions between the innate and adaptive immune systems. Its performance on a realistic process anomaly detection problem is shown to be better than standard AIS methods (negative-selection), policy-based anomaly detection methods (systrace), and an alternative innate AIS approach (the DCA). In addition, it is shown that runtime information can be used in combination with system call information to enhance detection capability.
Year
DOI
Venue
2010
10.2139/ssrn.2823358
INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING
Keywords
DocType
Volume
Second generation Artificial Immune Systems, innate immunity, process anomaly detection, intrusion detection systems
Journal
6
Issue
ISSN
Citations 
3-4
1548-7199
9
PageRank 
References 
Authors
0.52
7
3
Name
Order
Citations
PageRank
Jamie Twycross1907.51
Uwe Aickelin21679153.63
Amanda M. Whitbrook3757.56