Network Security: Threat Model, Attacks, and IDS Using Machine Learning
Divya Kapil, Nidhi Mehra, Atika Gupta, Sudhanshu Maurya, Anupriya Sharma Ghai
Abstract
Nowadays, computer technology has become necessary in our day-to-day life in various aspects such as communication, entertainment, education, banking, etc. In the digital era Network, security is essential, and the most challenging issue is identifying the intrusion attacks. An intrusion Detection System is a technique that monitors the network for anomalous activities and when these actions are discovered, then it generates an alert. An intrusion Detection System analyses big data due to heavy traffic and it protects data and computer networks from malicious actions. So, a fast and efficient classification technique is required to classify the normal and suspicious activities. For intrusion detection, various techniques have come into existence that leverage the machine learning approach. Various machine learning-based IDS techniques are described and categorized in this paper. Also, this research work presents a threat model in various networking layers. For experimental analysis, the NSL_KDD dataset are used and Naïve Bayes, Random forest, and J 48 classification algorithms are used and the results are shown for TPR, precision FPR, F-measure, recall parameters.