Packet Header Anomaly Detection Using Bayesian Belief Network

Abstract

This research paper presents a packet header anomaly detection approach by using Bayesian belief network which is a probabilistic machine learning model. A DARPA dataset was tested for the performance evaluation in the packet header anomaly detection or DoS intrusion-type. In this respect, the proposed method using Bayesian network gives an outstanding result determining a very high detection rate of reliability at 99.04 % and precision at 97.33 % on average.