REVOLUTIONIZING INTRUSION DETECTION: THE ROLE OF MACHINE LEARNING IN NEXT-GENERATION NETWORK SECURITY

Abstract

Cyber threats keep to conform, and conventional intrusion detection systems struggle to provide well timed and correct hazard mitigation. Machine getting to know has emerged as a transformative technique to improving community safety through enabling real-time anomaly detection, predictive threat modeling, and adaptive defense mechanisms. This paper explores the position of machine getting to know in subsequent-era intrusion detection structures, highlighting its ability to analyze widespread datasets, perceive complicated assault patterns, and reduce false positives. Supervised and unsupervised getting to know techniques, together with deep mastering frameworks, have appreciably stepped forward the performance of intrusion detection through distinguishing between legitimate and malicious community conduct. Additionally, the combination of reinforcement getting to know allows computerized response mechanisms, improving the resilience of protection infrastructures. However, challenges including hostile assaults, version interpretability, and computational overhead should be addressed to make sure dependable deployment. By leveraging device gaining knowledge of-pushed answers, groups can enhance their cybersecurity frameworks and proactively mitigate emerging threats. This look at underscores the need of continuous improvements in AI-pushed security to counteract sophisticated cyberattacks and make sure robust network protection techniques.