Smart Intranet Security: Behavioral Attack Detection Using Machine Learning

Abstract

In the rapidly evolving landscape of cybersecurity, traditional defenses are often inadequate against 
sophisticated threats. This paper presents a novel approach to intranet security through behavioral attack 
detection using machine learning techniques. By analyzing user and entity behavior within an organization’s 
network, we develop a system that identifies anomalous patterns indicative of potential security breaches. 
Leveraging supervised and unsupervised learning algorithms, our model is trained on diverse datasets that 
simulate both normal and malicious activities. The results demonstrate a significant improvement in detection 
accuracy and response times compared to conventional methods. This approach not only enhances real-time 
threat identification but also minimizes false positives, thereby allowing security teams to focus on genuine 
threats. Ultimately, our findings underscore the efficacy of machine learning in strengthening intranet security 
frameworks and provide a foundation for future research in adaptive cybersecurity solutions.