Enhancing Network Security Through Machine Learning Techniques in Modern Communication Systems
Keywords:
Machine Learning, Network Security, Cybersecurity, Anomaly Detection, Intrusion Detection, Communication SystemsAbstract
In the digital era, network security remains a significant challenge due to the increasing sophistication of cyber threats. Traditional security mechanisms often fail to provide robust protection against evolving attacks. Machine Learning (ML) has emerged as a promising approach for detecting and mitigating security threats in modern communication systems. This paper explores the role of ML in network security, its applications in intrusion detection, and its effectiveness in anomaly detection. The study further analyzes various ML algorithms used for security enhancement, including supervised, unsupervised, and reinforcement learning models. Challenges such as adversarial attacks, data privacy concerns, and computational costs are also discussed. Experimental results and case studies demonstrate the efficiency of ML-based security mechanisms compared to traditional methods. The findings suggest that ML can significantly improve security frameworks by providing real-time threat detection and response capabilities.
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