Advanced Machine Learning Techniques for Detecting Behavior-Based Intranet Threats

Abstract

Detection of attacks occurring within an Intranet using a behaviour-based machine learning approach. The detection of intranet intrusions is made difficult by the new and emerging malicious behaviours and attacks on system internets. Hence, the concept behind the proposed network-stage approach is the combination of machine learning techniques with behaviour-based detection techniques. This would require exploiting machine learning algorithms to seek application in identifying intranet attacks based on user behavioural patterns, to be analysed alongside a given network-based traffic and the concerned system logs. The model thus learns to discriminate normal from anomalous behaviours, thus ensuring proactive response mechanisms for threat detection. The approach thus defined could offer a very feasible pathway to extending the detection features and adaptive defense mechanism for intranets concerning the security posture of such environments to real-time detection. The experimental evaluations and comparison analyses prove its effectiveness and indicate that it could be easily integrated into the extant security framework to enhance the internetworks against new threats. The proposed system also includes feature engineering methods for retrieval of important patterns in terms of behaviour from network and system data. Thus, it will do more efficiency in anomaly detection. The model learns continuously from the evolving behaviours in the network and adapts to new and unknown attack strategies. It helps keep the dynamic approach based and allows the system to focus on broad intranet threats, making it highly suitable for modern cyber infrastructures toward which security focuses.