Comparison of Bayesian Networks and Lazy Model 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 great challenges. Intrusion detection systems are a major component of a secure network. Traditional intrusion detection systems cannot adapt themselves to 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 increasing network security. In this paper, we compare the performance of the different lazy model-based algorithms and Bayesian networks on their data sets. Obtained results show that the HNB algorithm has the highest precision of 83.29% for the intrusion detection system.