摘要
传统算法在网络入侵检测方面存在部分问题,为了进一步提升检测水平,在网络信息攻击手段日益增多的背景下,提出了一种基于最邻近结点(K-NearestNeighbor,KNN)算法的网络入侵检测技术方法。该方法将粒子优化解决局部极值问题,以实现改善网络入侵检测技术的目的。测试结果表明,基于KNN算法的网络入侵检测技术能够较好地识别攻击类型,其误检率显著优于Rabin-Karp、Boyer-Moore、Colussi这3种传统算法,验证了算法的有效性,能够较好地应用于网络入侵行为的预测,表现出良好的预测精度。
Traditional algorithms have some problems in network intrusion detection,and in order to further improve the detection effect,a method of network intrusion detection technology based on the nearest node K-Nearest Neighbor(KNN)algorithm is proposed in the context of the increasing means of network information attacks.The method will particle optimization to solve the local extremum problem to achieve the purpose of improving network intrusion detection technology.The test results show that the network intrusion detection technique based on KNN algorithm can better identify the type of attacks and its false detection rate is significantly better than the three traditional algorithms of Rabin-Karp,Boyer-Moore and Colussi,which verifies the effectiveness of the algorithm and can be better applied to the prediction of network intrusion behavior,showing good prediction accuracy.
作者
吴晟懿
WU Shengyi(Tianzhu County Secondary Vocational School,Qiandongnan Guizhou 556699,China)
出处
《信息与电脑》
2023年第5期67-69,共3页
Information & Computer
关键词
KNN算法
网络入侵检测
粒子群落
迭代
K-Nearest Neighbor(KNN)algorithm
network intrusion detection
particle community
iteration
