Intrusion Detection System: Comparative Analysis of Supervised and Unsupervised Techniques

With advancements in technology, the rapid growth in cybercrimes poses crucial challenges to maintaining the security and integrity of computer networks. Signature-based techniques and predefined rules are the traditional methods for Intrusion Detection Systems, which are inadequate for handling emerging cybercrimes. This paper presents a comparative analysis of supervised and unsupervised machine learning techniques in Intrusion Detection Systems. Various machine learning models, such as supervised and unsupervised learning, are used to review the limitations of traditional IDS approaches. Overcoming these challenges in conventional Intrusion Detection Systems is the key concept behind this paper. In the supervised learning method we have used Support Vector Machine, Decision Tree and Random Forest and for unsupervised learning algorithm in intrusion detection, we use Principal Component Analysis, K-Means method and DBSCAN. This work discusses the implications of adopting machine learning in intrusion detection and suggests potential areas for future research, such as the integration of deep learning techniques and the development of an adaptive intrusion detection system that evolves with emerging threats. The outcomes are meant to help in the development of more sophisticated and effective intrusion detection systems that are capable of handling novel cyber-attacks.