A HIBRID INSIDER THREAT DETECTION SCHEME (AHITDES) USING THE INTEGRATION OF MISUSE AND ANOMALY DETECTION IN MANUFACTURING NETWORK SYSTEMS

In the evolving landscape of manufacturing network systems, cybersecurity is paramount to safeguard against insider threats that can compromise sensitive data, operations, and intellectual property. The "A Hybrid Insider Threat Detection Scheme" (AHITDES) project represents a pioneering approach to fortify the security of manufacturing networks. AHITDES leverages a comprehensive fusion of two potent cybersecurity methodologies: misuse detection and anomaly detection. By integrating these approaches, the system creates a robust and adaptable defense mechanism against both known and unknown insider threats. Misuse detection algorithms identify predefined patterns of malicious behavior, while anomaly detection algorithms scrutinize deviations from established baselines. This amalgamation empowers AHITDES to detect not only known attacks but also novel, stealthy, or sophisticated insider threats. Focused on manufacturing network systems, this project is set to redefine the security paradigm in industrial settings. AHITDES's proactive and adaptive threat detection promises to bolster the resilience of manufacturing networks, safeguarding crucial data and processes in an era of increasing cyber threats.