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DDo S攻击一直严重威胁着网络的安全,针对DDo S攻击的检测与防御是网络安全研究中的一个重要课题.利用蛋白质相互作用网络中的功能预测特点,设计了一个DDo S攻击检测及追踪方案.该方案在分时统计的基础上创建RTCT值,并在服务器端根据不同的RTCT值来生成不同的个体,利用特征熵值构建个体蛋白质相互作用网络,并通过与目标蛋白质相互作用网络对比来检测是否有攻击发生;当有攻击发生的时候,分解RTCT值并锁定攻击源.该方案对DDo S攻击的检测较为敏感,在复杂的网络拓扑结构中能够准确的检测出DDo S攻击并锁定攻击源,模拟实验研究结果表明了该机制是高效的.
DDoS attack has always been a serious threat to the network security, and the detection and defense of DDoS attacks is an important issue in network security research.DESA is used to design a DDoS attack detection and tracing scheme The scheme creates RTCT values based on time-sharing statistics and generates different individuals according to different RTCT values on the server side, constructs an individual protein-protein interaction network by using characteristic entropy and compares them with the network of protein interactions It detects the attack if it occurs and decomposes the RTCT value and locks the attack source when an attack occurs.The scheme is sensitive to the detection of DDoS attacks and can accurately detect DDoS attacks and lock down attacks in complex network topologies The results of source and simulation experiments show that this mechanism is efficient.