Databases Reference
In-Depth Information
Chapter 5
AN EXTENDED BAYESIAN/HAPSO INTELLIGENT
METHOD IN INTRUSION DETECTION SYSTEM
S. DEHURI
∗
and S. TRIPATHY
†
∗
Department of Information and Communication Technology,
Fakir Mohan University, Vyasa Vihar,
Balasore-756019, ORISSA, India
satchi.lapa@gmail.com
†
Department of Computer Science and Engineering,
Indian Institute of Technology, Patna, India
somanath@gmail.com
This chapter presents a hybrid adaptive particle swarm optimization
(HAPSO)/Bayesian classifier to construct an intelligent and more compact
intrusion detection system (IDS). An IDS plays a vital role of detecting
various kinds of attacks in a computer system or network. The primary
goal of the proposed method is to maximize detection accuracy with a
simultaneous minimization of number attributes, which inherently reduces the
complexity of the system. The proposed method can exhibits an improved
capability to eliminate spurious features from huge amount of data aiding
researchers in identifying those features that are solely responsible for achieving
high detection accuracy. Experimental results demonstrate that the hybrid
intelligent method can play a major role for detection of attacks intelligently.
5.1. Introduction
An intrusion detection system (IDS) is a program to detect various kinds
of misuse in computer system or network. An intrusion is defined as
any non-empty set of actions that attempt to compromise the integrity,
confidentiality or availability of a resource. Intrusion detection can be
grouped into two classes such as misuse intrusion detection and anomaly
intrusion detection.
1
Misuse intrusion detection uses well defined patterns
of the attack and exploit weaknesses in system and application software
to identify the intrusions. These patterns are encoded in advance and used
to match against the user behavior to detect intrusions. Anomaly intrusion
detection uses the normal usage behavior patterns to identify the intrusions.
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