Machine Learning Algorithms for Intrusion Det...

Machine Learning Algorithms for Intrusion Detection Systems

Author Name : Nazeer Shaik, Dr. C. Krishna Priya, Dr. P. Sumalatha

ABSTRACT This paper explores the integration of Machine Learning (ML) algorithms into Intrusion Detection Systems (IDS) to enhance network security and mitigate cyber threats. Recent advancements in ML techniques, including deep learning, ensemble learning, and hybrid approaches, are reviewed to provide insights into their applicability in intrusion detection. Existing IDS systems such as Snort, Suricata, and OSSEC are analyzed, highlighting their strengths and limitations. The proposed system incorporates advanced ML algorithms, including Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN), to improve detection accuracy and efficiency. Through comprehensive data preprocessing, feature selection, and model integration, the proposed system demonstrates superior performance in detecting intrusions, as evidenced by comparative analysis results. Future enhancements in model interpretability, adversarial robustness, privacy-preserving techniques, and realworld deployment are identified as key areas for further research and development. By continuing to innovate and refine IDS technologies, we can build a more secure digital environment and safeguard against the evolving landscape of cyber threats.