Adaptive neuro-fuzzy intrusion detection systems

被引：54

作者：

Chavan, S

Shah, K

Dave, N

Mukherjee, S

Abraham, A

Sanyal, S

机构：

来源：

ITCC 2004: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: CODING AND COMPUTING, VOL 1, PROCEEDINGS | 2004年

关键词：

D O I：

10.1109/ITCC.2004.1286428

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Intrusion Detection System architecture commonly used in commercial and research systems have a number of problems that limit their configurability, scalability or efficiency. In this paper, two machine-learning paradigms, Artificial Neural Networks and Fuzzy Inference System, are used to design an Intrusion Detection System. SNORT is used to perform real time traffic analysis and packet logging on IP network during the training phase of the system. Then a signature pattern database is constructed using protocol analysis and Neuro-Fuzzy learning method. Using 1998 DARPA Intrusion Detection Evaluation Data and TCP dump raw data, the experiments are deployed and discussed.

引用

页码：70 / 74

页数：5

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