Analysis on Protocol-Based Intrusion Detection System Using Artificial Intelligence

One of the major challenges in every field is the network security, so for preventing system, its data and sensitive information from any unauthorized access or harmful activity, intrusion detection systems are used. The objective of this research work is threefold. First objective is to applying various machine learning approaches such as Bayes classifier and random forests on the intrusion detection system for detecting any type of malicious activity. Second objective is to do the comparison for the accuracy of both random forest and Bayes classifier method. Third objective is to find out which algorithm will be fast and provide best result for intrusion detection systems to detect various attacks. In this, working on a protocol-based intrusion detection system understands the HTTP that is running in the particular net server or system. It can be used on the online server which is monitoring the HTTP or HTTPS. As we know that HTTP is a basic protocol that is used for communication between the client and server, attackers can exclusively make use of these protocols to exploit web application vulnerabilities. This system will analyze and monitor the dynamic state and behavior of the protocols, and therefore protecting the system. In this research work, acting on different classification algorithm’s performance like: Bayesian network, Naive Bayes, random forest and random tree. Selection of the features can reduce the information as well as the computational complexity thus producing the more efficient results.