高速超高带宽网络入侵检测模型仿真  

Simulation of Intrusion Detection Model of High Speed and Ultra-High Bandwidth Network

作　　者：李敏[1] 郭磊[1] 宋倬[2] 

机构地区：[1]安阳师范学院软件学院,河南安阳455000 [2]沈阳师范大学计算机与数学基础教学部,辽宁沈阳110000

出　　处：《计算机仿真》2016年第6期266-269,共4页Computer Simulation

摘　　要：在网络入侵优化检测中,由于高速超高带宽网络存在带宽大、速度快的特点,造成网络入侵后病毒变异和感染的速度更快,传统的检测方法运算较为复杂,需要进行大量的迭代计算,导致无法适应高速超高带宽网络入侵检测。提出基于遗传聚类的网络入侵检测检测算法即NIDBGC算法。由Leader聚类阶段和遗传优化两个阶段组成。通过Leader聚类阶段对入侵数据进行初步分类,以降低计算复杂程度,通过遗传优化算法利用编码与解码来实现高速超高带宽网络空间与解之间的映射,用交叉变异和选择对检测数据种群进行优化。实验对网络入侵的检测。仿真结果表明,高速超高带宽网络的平均检测大于50%,误检率约为1.5%,充分表明NIDBGC算法的有效性,为网络入侵优化检测提供了参考。A network intrusion detection algorithm based on genetic clustering, namely NIDBGC algorithm is pro- posed, which is composed by two stages of Leader clustering stage and genetic optimization. Preliminary classification of intrusion data is carried out by Leader clustering stage in order to reduce the computational complexity. On the ba- sis of genetic optimization algorithm, encoding and decoding are used to realize the mapping between the space and the solution of high speed and ultra - high bandwidth network. Cross mutation and selection are applied to optimize the detected data population, so as to realize the detection of network intrusion. The simulation results show that the average detection of high speed and ultra - high bandwidth network is more than 50%, and the false detection rate is about 1.5%. The validity of NIDBGC algorithm is sufficiently demonstrated, which can provide a reference for opti- mization detection of network intrusion.

关 键 词：高速超高带宽 网络入侵 检测 

分 类 号：TU391[建筑科学—结构工程]