Intrusion Detection System Using Machine Learning

In recent years, the field of network security has witnessed severe advances in the development of intrusion detection systems to protect computer networks from malicious activities and unauthorized access. Intrusion Detection Systems are critical components of cyber and network security; their effectiveness had been augmented through the utilization of machine learning algorithms widely. This study presents an evaluation of various machine learning algorithms for detection of intrusions or attacks on a network, with the focus on identifying the strengths and weaknesses of each algorithm. The methodology employed in this project involves preprocessing the dataset to handle missing values, feature scaling, and class imbalance. With the use of machine learning, an intrusion detection system can adapt to any new attack patterns, learn from existing data, and provide real-time responses. The outcome of this project holds significant potential for further research.