Advanced Machine Learning Techniques for Proactive Intranet Security and Threat Detection

Detecting intranet-based attacks poses significant challenges in the realm of cybersecurity, particularly due to the adaptive and evolving strategies employed by attackers. This paper presents an advanced method for identifying behavior-based intranet attacks using cutting-edge machine learning techniques. Through the use of machine learning algorithms, the method seeks to precisely identify and eliminate intranet threats by examining behavioral anomalies. The system is trained to distinguish between normal and anomalous activities by evaluating network traffic and system logs, enabling early detection and proactive threat mitigation. This novel technique bolsters intranet security by offering robust detection mechanisms and dynamic defence strategies. Experimental results and comparative analyses illustrate its efficacy, highlighting its potential to enhance cybersecurity frameworks and fortify intranet defences against future attack vectors. The suggested method presents a viable option for improving the security framework of intranet environments through real-time detection and adaptive defence mechanisms. The suggested approach utilizes machine learning algorithms to accurately identify and mitigate intranet assaults based on their behavioural patterns.