Title

Intrusion Detection Using Radial Basis Function Network on Sequences of System Calls

Abstract

Over the past few years, security has been an increasing concern, with the growth of network and technological development. An intrusion detection system is a critical component for secure information management. Unfortunately, present IDS's falls short of providing protection required for growing concern. Creation of an IDS to detect anomaly intrusions, in a timely and accurate manner, has been an elusive goal for researchers. This paper describes a host-based IDS model, utilizing a Radial Basis Function neural network. It functions as a combined anomaly/misuse detector that helps to overcome most of the limitations in existing models. Rather than creating user profiles or behavioral characteristics, we trained our network using session data in the identification and tested experimentally on different attack/normal sessions. These results suggest that training the IDS on session data is not only effective in detecting intrusions, but also accurate and timely.

Meeting Name

2003 International Joint Conference on Neural Networks (2003: Jul. 20-24, Portland, OR)

Department(s)

Electrical and Computer Engineering

International Standard Serial Number (ISSN)

1098-7576

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


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