本文選題：模糊網絡 + 入侵檢測　； 參考：《現代電子技術》2017年10期

【摘要】：模糊網絡中入侵特征較為多樣化,無法通過固定的閾值進行合理判斷。為了解決模糊網絡入侵檢測方法存在檢測率低、誤報率高和檢測速度慢等問題,提出一種基于量子神經網絡的層序列特征自動提取方法。在該算法中,通過對模糊網絡進行層次劃分,運用量子BP神經網絡模型以量子形式形態(tài)的空間思維結構來提取信息,通過量子空間結構中量子門的移位與旋轉變化對神經網絡量子形態(tài)相位進行操作,完成多層序列特征自動提取。仿真實驗表明,該算法具有較好高的檢測率和檢測效率,并且誤報率較低。
[Abstract]:The intrusion features in fuzzy networks are diverse and can not be reasonably judged by fixed thresholds. In order to solve the problems of low detection rate, high false alarm rate and slow detection speed in fuzzy network intrusion detection method, a layer sequence feature automatic extraction method based on quantum neural network is proposed. In this algorithm, the fuzzy network is divided into layers, and the quantum BP neural network model is used to extract the information from the spatial thinking structure of the quantum form. The phase of quantum morphology in neural network is operated by the shift and rotation of quantum gate in quantum space structure, and the multi-layer sequence feature is automatically extracted. Simulation results show that the algorithm has high detection rate and detection efficiency and low false alarm rate.
【作者單位】： 武漢大學經濟與管理學院;義烏工商職業(yè)技術學院機電信息學院;
【基金】：浙江省2015年度高等教育教學改革項目(JG2015343)
【分類號】：TP393.08
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本文編號：2066922