A Survey on Intrusion Detection System using Machine Learning Techniques

Abstract

Abstract: In every part of the world, there is a tremendous growth in digital literacy in the present era. People are trying to access internet-based applications with the use of digital machines. As a result, the internet as become a primary requirement for everyone, and most business transactions often take place conveniently across the network. On the other hand, intruders involved in making intrusions and doing activities such as capturing passwords, compromise on the route, collecting details of credit cards, etc. Many malicious activities are taking place over the network due to this intruding activity on the internet. The intrusion detection system (IDS) helps to find the attacks on the system and the intruders are detected. This paper has expected for an approach to develop IDS by using the principal component analysis (PCA) and the random forest classification algorithm. Where the PCA will help to shape the dataset by reducing the dimensionality of the dataset and the random forest will help in classification. Results obtained states that the proposed approach works more resourcefully in terms of accuracy as compared to other methods like SVM, Naïve Bayes, and Decision Tree.