Authors : Rohit Vayugundla Rao; Saksham Kumar

Volume/Issue : Volume 8 - 2023, Issue 8 - August

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://tinyurl.com/yz2mab2k

DOI : https://doi.org/10.5281/zenodo.8336942

Abstract : The research paper explores the utilization of machine learning techniques to enhance anomaly detection and intrusion detection systems in the realm of cybersecurity. The study aims to improve the capability of identifying and responding to cyber threats more effectively. The paper begins with an overview of the evolving cybersecurity threat landscape, highlighting the need for advanced detection mechanisms. Traditional methods' limitations lead to an exploration of machine learning's potential in addressing these challenges. The literature review delves into traditional anomaly detection and intrusion detection techniques, revealing their shortcomings in adapting to dynamic threats. The role of machine learning in cybersecurity is examined, showcasing its potential to uncover subtle anomalies and unknown attack patterns. Existing studies in the field are analyzed, emphasizing the combination of multiple machine learning techniques to overcome limitations. Sections focusing on specific machine learning approaches—supervised, unsupervised, and semi- supervised—detail their applications in anomaly detection. Real-world integration considerations, including data preprocessing, model selection, real-time monitoring, and ethical concerns, are explored. Case studies and experiments illustrate the practical application of machine learning in cybersecurity, bridging theoretical concepts with practical implementation. Recommendations and best practices guide the implementation of machine learning techniques, emphasizing the importance of continuous learning, collaboration, and ethical considerations. Future directions, including federated learning and quantum computing's impact, highlight the evolving landscape of cybersecurity.