Enhancing Cyber Security Through Machine Learning: A Comprehensive Analysis
Pagare Anuradha Machhindra, Bhatkudav Nikita Vijay, Borawake Samrudhi Mahendra, Cholke Anushka Rahul, Pangavhane Aditya Anil, P. Sunil · 2023
In our increasingly digital world, cyber security has become a paramount concern, with threats evolving from malicious software to sophisticated hacking techniques. To effectively combat these challenges, the integration of machine learning techniques has garnered significant attention and relevance. This comprehensive analysis delves into the potential of leveraging machine learning to fortify cyber security, discussing the utilization of predictive analytics, anomaly detection, and threat intelligence. Machine learning applications offer a multifaceted approach to identifying and mitigating cyber threats. Predictive analytics harness historical data to anticipate potential security breaches, while anomaly detection techniques scrutinize deviations from established norms, aiding in real-time threat detection. Furthermore, the integration of threat intelligence allows organizations to stay ahead of evolving threats. This analysis also addresses the challenges and ethical considerations associated with deploying machine learning in cyber security, emphasizing the importance of responsible AI practices. By offering insights into the current state of the field and future prospects, this comprehensive analysis serves as a valuable resource for cyber security professionals, researchers, and policymakers, enabling them to strengthen defenses against the ever-evolving landscape of cyber threats. It underscores the significance of continued research and implementation of machine learning in safeguarding our digital ecosystem.