Building an Efficient Intrusion Detection System using Feature Selection and Machine Learning
Akshay Kaushik, Varun Goel · International Journal for Research in Applied Science and Engineering Technology · 2022
Abstract: With the increase in internet activity and as the world goes digital as the days go, the risk of exposure to malicious activities also increased rapidly. The intruders/hackers use various methods to gain unauthorized access to one's computer or any other device, Network Intrusion is one of the methods by which intruders attack the network of the user, the user can be an individual or an organization based on the intention/agenda of the attackers. Significant Reasons for intrusion are Hacktivism, Steal Money or Data, and Spying. Due to the internet being a vast place, it is challenging to pinpoint a particular way in which Network Intrusion takes place, therefore a Network Intrusion Detection System needs to be put in place in order to deal with the issues regarding Network Intrusions. There are multiple leaks or data extortion that happened previously and, in this paper, the dataset released based on a leak from KDD99 is used. An improved version of KDD99 (NSL-KDD) is used in this study. NSLKDD datasets have been used for training the Machine Learning Model. Given the number of attributes in the dataset, it was difficult to use all attributes so, feature selection methods were used to get the best attributes to develop an efficient Machine Learning model. In this analysis of Machine Learning algorithms, the algorithms under consideration are Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and Naive-Bayes. For comparison of the performance of the algorithms metrics like Accuracy Score, Confusion Matrix, and Classification Report were considered to find the best algorithm among them.