A Privacy Preserving Context Sensitive Kernel

Insider threats and attacks have witnessed a concerning rise in recent years, posing significant risks and financial implications for businesses. Malicious insiders exploit memory dumping tools to leak data, resulting in a substantial surge in data breach incidents. Evaluating the true extent of damage caused by insider attacks proves challenging, particularly in defence scenarios. This research presents a novel architecture designed to prevent data leaks by intercepting memory dump calls and generating privacy-preserved context-sensitive dumps. By ensuring that such dumps do not con- tain significant sensitive data, the architecture mitigates the risk of data leakage by malicious insiders. In this approach, unauthorized attempts to generate memory dumps using popular tools like ProcDump are directed to the proposed program, which creates privacy-preserving context-sensitive dumps, convincingly resembling legitimate memory dumps generated by the original tools. The prevention of sensitive data leaks is achieved while upholding organizational privacy through the utilization of a deep learning model. The attacker is deceived as the memory dump generated contains only non-sensitive user information in the device. The feasibility and effectiveness of this innovative approach are demonstrated through a military use case within the proposed architecture.