【摘要】：為了提高工控系統(tǒng)入侵的檢測率,討論了傳統(tǒng)工控入侵檢測技術的原理,并從信息論的觀點進行了對比研究.通過對工控系統(tǒng)特異性及其攻擊手法的建模,歸納出工控攻擊在協(xié)議棧、統(tǒng)計特性、通信行為等方面表現(xiàn)出的動態(tài)和靜態(tài)指紋,基于一種新的異構信息的抽象方法,提出并實現(xiàn)了一個基于組合神經網絡的啟發(fā)式工控系統(tǒng)異常檢測模型.測試結果表明該檢測模型運行高效,相比一般智能方法檢測結果更為準確.
[Abstract]:In order to improve the intrusion detection rate of industrial control system, the principle of traditional industrial control intrusion detection technology is discussed, and a comparative study is carried out from the point of view of information theory. Through the modeling of the specificity of industrial control system and its attack techniques, the dynamic and static fingerprints of industrial control attack in protocol stack, statistical characteristics, communication behavior and so on are summarized, which is based on a new abstract method of heterogeneous information. An anomaly detection model of heuristic industrial control system based on combined neural network is proposed and implemented. The test results show that the detection model is efficient and more accurate than the general intelligent method.
【作者單位】： 四川師范大學網絡與通信技術研究所;
【基金】：四川省教育廳青年基金(15ZB0026)
【分類號】：TP183;TP393.08

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