一种基于接口异常度可信判断的内容中心网络缓存污染防御方法

针对内容中心网络的缓存污染攻击问题,提出一种基于接口异常度可信判断的限速机制,根据接口异常度检测缓存污染攻击类型,并结合接口命中率对异常接口进行限速控制.仿真结果表明,该机制可以同时防御缓存恶意侵占(Locality-Disruption)和虚假内容缓存(False-Locality)2种攻击,且通过引入False-Locality攻击的可信度判断,可大概率区分异常的False-Locality攻击和正常的突发拥塞事件,避免错误抑制Flash Crowd网络行为.

A Cache Pollution Defense Mechanism Based on Trust Judgment of Face Abnormality in Content Centric Networking

To solve the problem of cache pollution attack in content centric networking, a traffic limiting control mechanism was proposed based on trusted judgment of face abnormality. By detecting the abnor-mality status of each face, the traffic of abnormal face was limited according to its request hit probability. Simulations show that, this mechanism can defend against both locality-disruption and false-locality at-tacks. In addition, using the trust judgment of face abnormality, the false-locality attack can be distin-guished from the flash crowd event with large probability. So, it can avoid mistakenly restraining the nor-mal network behavior.