Behavioral Attack Detection in Intranet Network Using Advanced Machine Learning Techniques

Abstract

In the cybersecurity landscape, detecting intranet attacks remains particularly challenging as hostile tactics constantly adapt and evolve. This research introduces an innovative machine learning approach that identifies potential threats by analyzing behavioral patterns rather than relying on fixed signatures. The methodology harnesses advanced algorithms to recognize and counter intranet-based attacks by identifying anomalous behaviors that deviate from established usage patterns. By examining network traffic and system logs, our model differentiates between normal and suspicious activities, enabling it to detect and respond to threats proactively. This approach shows significant promise for strengthening intranet security through its real-time monitoring capabilities and adaptive defense systems. The solution enhances security posture by continuously analyzing behavior patterns and identifying potential threats before they can cause damage. Our empirical evaluations and comparative analyses confirm the model's effectiveness. Test results demonstrate how it successfully identifies anomalies that traditional security measures might miss, while maintaining a low rate of false positives. This technology complements existing cybersecurity frameworks rather than replacing them, providing an additional layer of protection. The integration creates a more robust defense system for intranet environments, particularly against sophisticated and evolving threats.