Intrusion Detection in Network Systems Using Machine Learning Algorithms

Intrusion detection in network systems is a critical component for maintaining cybersecurity and protecting data integrity. This paper explores the application of various machine learning algorithms to identify and classify network intrusions effectively. By leveraging supervised and unsupervised learning techniques, the study aims to enhance detection accuracy while minimizing false positives. Experimental results demonstrate the efficiency of algorithms such as decision trees, support vector machines, and neural networks in analyzing network traffic and detecting malicious activities in real time. The integration of machine learning in intrusion detection systems promises improved adaptability and robustness against evolving cyber threats.