Identification and Classification of Network-Based Cybersecurity Intrusion Activities Through Machine Learning Algorithms

Due to internet usage, people have experienced rapid changes. The fact is that the internet helps people to manage their social networks and links for their assistance whenever they require it. When users connect to the internet, they share their personal and professional data across the network with several risks with each other or any organization. As all know the internet is a crucial aspect of an individual’s day-to-day life, our data could be at risk at any time. For this purpose, the concept of intrusion detection system (IDS) may be proposed. IDS is important for protecting internet users against malicious or unauthorized attacks. An intrusion detection system monitors suspicious activity in network traffic, if it finds any issues or illegitimate activity, it immediately issues an alert message. In this paper, the main focus will be on different machine learning classification techniques; starting with machine learning algorithms KNN, logistic regression, decision tree, etc. These algorithms are used for finding the best accuracy or other performance metrics using the NSl-KDD datasets in the first step. Based on the first step’s result, the second process is the database with the most versed algorithm. Authors have considered NSL-KDD datasets for experiments and evaluating the model performance, and it ensures the efficient efficiency of our proposed model. The main focus was to improve the accuracy level of the model. Experiments on datasets implemented demonstrate different classifications through which authors find that decision trees provide the best result.