A Machine Learning-based Intrusion Detection Framework for Secure Communication in Next- Generation Computer Networks

The rapid expansion of computer networks and digital communication systems has increased the risk of cyber-attacks and network intrusions. Traditional intrusion detection systems rely mainly on predefined signatures to identify threats, which limits their ability to detect new and evolving attacks. Machine learning techniques provide an effective solution by enabling intelligent analysis of network traffic and identifying abnormal patterns. This paper presents a machine learning-based intrusion detection framework designed to enhance security in next-generation communication networks. The proposed system analyzes network traffic data and classifies activities as normal or malicious using supervised machine learning algorithms. The framework includes stages such as data preprocessing, feature extraction, model training, and attack detection. Experimental evaluation is conducted using benchmark network security datasets to assess the performance of different machine learning algorithms. The results indicate that machine learning models significantly improve the accuracy and efficiency of intrusion detection compared with traditional methods. The proposed framework can contribute to strengthening secure communication in modern network environments.