MLCAC: Dynamic Authorization and Intelligent Decision-making towards Insider Threats

Nowadays, the situation of data security is ever-increasing severity, however, the most damaging security threats do not originate from malicious outsiders but from malfeasant and negligent insiders. Generally, insider threats are prone to cause incalculable losses and serious issues due to that the insiders have the authority to access sensitive information. Therefore, how to effectively prevent and respond to insider threats is a significant challenge. Undoubtedly, the key to defending against insider threats is restricting access permissions and optimizing access control policies in real-time. To enhance data security, we propose MLCAC, a multi-layered collaborative access control model, which focuses on protecting sensitive data. A key insight is the design of a decentralized optimization for domain and authority. MLCAC prohibits unauthorized behavior and continuously monitors access logs which are analyzed by using the co-occurrence matrix to make intelligent decisions and dynamically adjust the access control policy in real time. In experiments, we collected 12574 access logs about 1753 system software and analyzed the correlation between software by using the co-occurrence matrix algorithm, including 824 groups co-occurring software of which the highest frequency is 12. The experiments indicate that the accuracy of the policy generated by intelligent decision-making is 89.55%. Therefore, the algorithm is significantly efficient for intelligent decision-making, which is the foundation of automatically generating policies and dynamic authorization.