Quarterly (winter, spring, summer, fall)
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7 x 10, illustrated
ISSN
1064-5462
E-ISSN
1530-9185
2014 Impact factor:
1.39

Artificial Life

Spring 2015, Vol. 21, No. 2, Pages 93-118
(doi: 10.1162/ARTL_a_00162)
© 2015 Massachusetts Institute of Technology
An Artificial Bioindicator System for Network Intrusion Detection
Article PDF (841.39 KB)
Abstract

An artificial bioindicator system is developed in order to solve a network intrusion detection problem. The system, inspired by an ecological approach to biological immune systems, evolves a population of agents that learn to survive in their environment. An adaptation process allows the transformation of the agent population into a bioindicator that is capable of reacting to system anomalies. Two characteristics stand out in our proposal. On the one hand, it is able to discover new, previously unseen attacks, and on the other hand, contrary to most of the existing systems for network intrusion detection, it does not need any previous training. We experimentally compare our proposal with three state-of-the-art algorithms and show that it outperforms the competing approaches on widely used benchmark data.