An Artificial Bioindicator System for Network Intrusion Detection

被引：5

作者：

Blum, Christian ^{[1
,2
]}

Lozano, Jose A. ^{[1
]}

Pinacho Davidson, Pedro ^{[1
,3
]}

机构：

[1] Univ Basque Country UPV EHU, Dept Comp Sci & Artificial Intelligence, Paseo Manuel Lardizabal 1, San Sebastian 20018, Spain

[2] Ikerbasque, Basque Fdn Sci, E-48011 Bilbao, Spain

[3] Univ Santo Tomas, Escuela Informat, Concepcion, Chile

来源：

ARTIFICIAL LIFE | 2015年 / 21卷 / 02期

关键词：

Bioindicators; population of agents; ecological approach to biological immune system; network intrusion detection; SUPPORT VECTOR MACHINES; POPULATION BOTTLENECKS; IMMUNE-SYSTEM; SELECTION; DIVERSITY; EVOLUTION; FORMS; SELF;

D O I：

10.1162/ARTL_a_00162

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：93 / 118

页数：26

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