An Enhanced Network Security using Machine Learning and Behavioral Analysis with Voting Classifier - JAMI CHAITANYA; Ms.M.V.Bhuvaneswari (Utilitas Mathematica, 2025)
With exponential growth in Internet use, the prevalence of cyber attacks has increased sharply, required by robust intrusion detection systems to secure network. This study is a new approach of supervised machine learning aimed at increasing the network security by accurately classification of network traffic as harmful or benign. The model that uses a mixture of algorithms of supervised learning and function selection techniques maximizes the detection rate by identifying the appropriate functions and using advanced algorithms. The model's performance evaluation uses the NSL-KDD data file, recognized benchmark for algorithms of network traffic classification.