Intrusion Detection Systems to avoid Cyberattack using Machine Learning

Machine learning brings another perspective into intrusion detection systems. Conventional techniques stumble at the pace of fast-evolving cyber threats, so we delve into machine learning-based intrusion detection systems. In this paper, we survey different datasets using different models: supervised, unsupervised, and hybrid. The work presents feature selection, model optimization, and performance metrics. This paper handles well-known challenges connected with imbalanced data and risks of overfitting. Ensembling and deep learning realize stunning improvements over the traditional approaches. These findings offer in-depth views of practical ways in which machine learning can be integrated into network security with a clear way forward toward real adaptive resilient intrusion detection, underpinning the transformative role of ML in hardening defenses within the cyber domain against sophisticated attacks.