一种自学习增量更新的被动安全研究

针对主动安全与被动安全的特点分析后，提出了一种基于自学习形式化安全模型，模型引入入侵诱因激励机制，防御成本预算调配制定安全资源部署，入侵收益评价防御策略，达到增量更新安全形为检测。抽象出隐藏边界的被动防御策略的迭代算法，并给出算法最佳主动防御收敛性。利用安全模式应用于数据中心深度防御的配置策略，从而优化防御策略保护敏感的数据信息。

A Passive Security Research to Self-Learning Incremental Update

For the active safety and passive safety features analysis, it propose a formal security model based on self-learning, the introduction of invasive incentive model, the budget allocation of defense costs to develop safety resource deployment, intrusion defense strategy evaluation gains, reaching incremental update security form of testing. Abstract iterative algorithm to hide the border passive defense strategy of active defense and gives the best convergence of the algorithm. Use safe mode applied to the data center configuration strategy of defense in depth, in order to optimize defense strategy to protect sensitive data.