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为了有效判断网络数据包是否存在被攻击的可能性,在以往的研究基础上提出了一种新的检测算法DMPS(Detection method based of particle susarm)。首先该算法根据数据包属性的离散度定义了状态检测指标,并利用粒子群优化方法给出了标准差分布的计算流程,以此判断数据包的异常状况。最后,通过OPNET和Matlab进行仿真实验,深入研究了影响该算法的关键因素,同时对比了与其他算法之间的性能状况,结果表明DMPS具有较好的适应性。
In order to effectively judge whether the network packet is attacked or not, a new detection algorithm based on DMPS (Detection Method based of particle susarm) is proposed. First of all, the algorithm defines the state detection index according to the dispersion of packet attributes, and uses the particle swarm optimization method to give the calculation flow of the standard deviation distribution to judge the abnormal condition of the data packet. Finally, through the simulation experiments with OPNET and Matlab, the key factors affecting the algorithm are deeply studied, and the performance status with other algorithms is compared. The results show that the DMPS has better adaptability.