随机接入移动终端网络中非法行为检测仿真

Illegal Behavior Detection Simulation of Random Access in Mobile Terminal Network

在非法入侵检测问题的研究中,移动终端网络与传统网络有着很大的不同,其端口能够接纳较多的非可控外部设备网络,接入网络中的行为呈现不可控威胁性,传统的移动终端保护采用静态限制方法,运用各种规则限制外部非法行为,但是随着外部接入行为多样性增加,上述限制方法无法做到全面限制,入侵风险增加。为解决上述问题,提出了一种利用人工鱼群聚类融合算法的随机接入移动终端网络中非法行为检测方法。选择入侵特征距离较远的对象作为初始聚类的中心,对主成份进行分析,使同一种类中的元素的相似性达到极大值,并且使不同种类之间的相似性达到极小值,完成检测。实验结果表明,利用改进算法进行随机接入移动终端网络中的非法行为检测,能够有效提高检测率,降低漏检率。

This paper proposed a method for detecting illegal behavior of random access mobile terminal network based on artificial fish clustering fusion algorithm. First, the distant invasion objects were selected as the center of in- itial cluster, and its main ingredients were analyzed. So that the similarity of elements in same types reaches a maxi- mum value and the similarity between the different types reaches a minimum value, thereby completing the test. Ex- perimental results show that the improved algorithm for random access mobile terminal network to detect illegal acts, can effectively improve the detection rate and reduce the false negative rate.