Presenting a Model for Making a Comparison of Bayesian Networks and Decision Tree Algorithms in Intrusion Detection Systems-Based on Data Mining

By development of information technology, network security is considered as one of the main issues and has great challenges. Intrusion detection systems are a major component of a secure network. Traditional intrusion detection systems cannot adapt themselves to the new attacks thus todayʹs intrusion detection systems have been introduced based on data mining. Identifying patterns in large volumes of data is a great help to us. Data mining techniques by identifying a binary label (normal packet, abnormal packet) and specifying attributes by classification algorithms can recognize the abnormal data. Therefore, the precision and accuracy of intrusion detection systems will increase, thereby network security increases. In this paper, we present a proposed model that examines various decision tree algorithms and Bayesian networks on their data sets in which the results of simulation suggest that J48 algorithm has the highest precision of 85.49% for the intrusion detection system.