【摘要】：對網(wǎng)絡(luò)中入侵節(jié)點信息進行優(yōu)化檢測,能夠更好的保障網(wǎng)絡(luò)安全穩(wěn)定運行。對入侵節(jié)點信息檢測時,需要根據(jù)節(jié)點的最佳獲取路徑,得到SVM的最優(yōu)參數(shù),來完成對入侵節(jié)點信息的檢測。傳統(tǒng)方法利用蟻群尋覓網(wǎng)絡(luò)節(jié)點路徑,得到支持向量機參數(shù),但忽略了該參數(shù)的最優(yōu)化,導(dǎo)致對信息的檢測結(jié)果不準(zhǔn)確。提出基于屬性攻擊圖的入侵節(jié)點信息優(yōu)化檢測方法。定義網(wǎng)絡(luò)潛質(zhì)入侵的屬性攻擊圖,將具有入侵節(jié)點信息的復(fù)雜入侵信號分解為IMF單頻入侵信號,獲取網(wǎng)絡(luò)入侵檢測系統(tǒng)的狀態(tài)轉(zhuǎn)移方程,將蟻群理論和支持向量機參數(shù)相融合,將網(wǎng)絡(luò)入侵檢查率作為目標(biāo)函數(shù),并對螞蟻進行高斯異變,將最優(yōu)路徑上的節(jié)點連接起來得到SVM最優(yōu)參數(shù),以參數(shù)為依據(jù)完成對網(wǎng)絡(luò)入侵節(jié)點信息檢測。實驗證明,所提方法設(shè)計精確度高,可以有效的提升嵌入式計算機網(wǎng)絡(luò)入侵檢測精度。
[Abstract]:The detection of intrusion node information in the network can better ensure the safe and stable operation of the network. In the detection of intrusion node information, the optimal parameters of SVM should be obtained according to the optimal access path of the node to complete the detection of the intrusion node information. The traditional method uses ant colony to search the path of network nodes to obtain the support vector machine parameters, but the optimization of the parameters is ignored, which leads to inaccurate information detection results. An intrusion node information detection method based on attribute attack graph is proposed. The attribute attack graph of network potential intrusion is defined, the complex intrusion signal with intrusion node information is decomposed into IMF single frequency intrusion signal, the state transition equation of network intrusion detection system is obtained, and the ant colony theory and support vector machine parameters are fused. The network intrusion detection rate is taken as the objective function, and the ant is changed by Gao Si, and the nodes on the optimal path are connected together to obtain the optimal parameters of SVM, and the information of the network intrusion node is detected based on the parameters. Experiments show that the proposed method has high design accuracy and can effectively improve the accuracy of embedded computer network intrusion detection.
【作者單位】： 全球能源互聯(lián)網(wǎng)研究院;
【基金】：國家電網(wǎng)公司2015年科技項目(SGRIXTKJ【2015】614)
【分類號】：TP393.08
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本文編號：2326651