Abstract:
Most of the currently available network security
techniques are not able to cope with the dynamic and
increasingly complex nature of the attacks on distributed
computer systems. An automated and adaptive defensive tool is
imperative for computer networks. One of the emerging
solutions for Network Security is the Intrusion Detection System
(IDS). However, this technology still faces some challenges such
as low detection rates, high false alarm rates and requirement of
heavy computational power. To overcome these difficulties, this
paper proposes an innovative Machine Learning algorithm
called Boosted Modified Probabilistic Neural Network
(BMPNN) which utilizes semi-parametric learning model and
Adaptive boosting techniques to reduce learning bias and
generalization variance in difficult classification. In this paper,
BMPNN is implemented as a classifier to detect different types of
network anomalies in the KDD-99 benchmark. Extensive
experimental outcome indicates that the proposed BMPNN
outperforms other state-of-the-art learning algorithms in terms
of detection accuracy and model robustness at an affordable
computational cost.
