Detecting behavior-based intranet attacks using machine learning
E. Naga Prabhakar, DesireddyLohithSaiCharan Reddy, S. Suprathika, Mavillapati Niharika, P. Shireesha · 2025
Detecting intranet attacks in cybersecurity is a challenging task, especially due to the constantly evolving nature of intranet attack patterns. This paper proposes an improvement method for detecting intranet attacks with behavior base which is implemented by machine learning. This paper proposes the use of machine learning algorithms, to use their capabilities to discover and thwart intranet attacks, based on their behavioral patterns. Network traffic and system log analysis uses are leveraged to teach the model to recognize normal and abnormal behavior to thus trigger proactive threat detection and response mechanisms within the model. To improve the security posture in the intranet environments, the proposed approach seems promising utilizing techniques that include real-time detection and adaptive defense. Its effectiveness is evaluated and compared through empirical evaluations and the possibility of further extending current cybersecurity frameworks and strengthening defense for intranet against emergent threats is explored.