【摘要】：為了提高入侵系統(tǒng)的檢測率和檢測速度,論文提出一種基于灰色K均值聚類算法的集群入侵檢測模型。利用灰色關(guān)聯(lián)分析理論對(duì)原始數(shù)據(jù)進(jìn)行預(yù)處理,根據(jù)ηij=1/n-1∑n2ξij(k)計(jì)算相關(guān)度,再對(duì)原始數(shù)據(jù)集合進(jìn)行聚類;最后引入集群技術(shù),將GFKM算法裝入集群系統(tǒng)中的每個(gè)檢測結(jié)點(diǎn)形成集群入侵檢測模型。最后,通過仿真實(shí)驗(yàn)對(duì)該模型進(jìn)行了驗(yàn)證,結(jié)果表明,GSFK算法應(yīng)用于入侵檢測模型中出現(xiàn)的誤報(bào)率為0.31%,漏報(bào)率為0.34%,而且該模型呈現(xiàn)出較好的泛化性,應(yīng)用于網(wǎng)絡(luò)入侵檢測中具有較好的性能。
[Abstract]:In order to improve the detection rate and speed of intrusion detection system, a cluster intrusion detection model based on grey K-means clustering algorithm is proposed in this paper. The grey relation analysis theory is used to preprocess the original data, the correlation degree is calculated according to 畏 ij=1/n-1 鈭，

本文編號(hào)：2284484