【摘要】：對無線網(wǎng)絡(luò)傳輸數(shù)據(jù)節(jié)點(diǎn)入侵進(jìn)行準(zhǔn)確檢測,可保證無線網(wǎng)絡(luò)的安全運(yùn)行。進(jìn)行入侵檢測時(shí),應(yīng)將節(jié)點(diǎn)行為進(jìn)行量化后運(yùn)用預(yù)先設(shè)定的信譽(yù)閾值與網(wǎng)絡(luò)節(jié)點(diǎn)的信譽(yù)值比較完成檢測,但是傳統(tǒng)方法通過對無線網(wǎng)絡(luò)某一區(qū)域環(huán)境中變量自發(fā)性的臨界變化狀態(tài)進(jìn)行分析,找出最脆弱的網(wǎng)絡(luò)節(jié)點(diǎn)完成入侵檢測,但是無法將待檢測的節(jié)點(diǎn)信譽(yù)閾值與正常網(wǎng)絡(luò)節(jié)點(diǎn)的信譽(yù)值進(jìn)行比對,降低了入侵檢測的精確度。提出一種基于信譽(yù)機(jī)制的無線網(wǎng)絡(luò)傳輸數(shù)據(jù)節(jié)點(diǎn)入侵優(yōu)化檢測方法。首先,對無線網(wǎng)絡(luò)節(jié)點(diǎn)的行為屬性進(jìn)行分析,通過對節(jié)點(diǎn)的丟包率、轉(zhuǎn)發(fā)率等數(shù)據(jù)實(shí)現(xiàn)量化后構(gòu)建模型,分析不同節(jié)點(diǎn)所具有的不同屬性。然后,對不同屬性節(jié)點(diǎn)通過建立的信譽(yù)機(jī)制進(jìn)行分析判斷,將節(jié)點(diǎn)行為進(jìn)行量化后運(yùn)用預(yù)先設(shè)定的信譽(yù)閾值與網(wǎng)絡(luò)節(jié)點(diǎn)的信譽(yù)值比較,確定被檢測節(jié)點(diǎn)信譽(yù)值與預(yù)先設(shè)定閾值的大小關(guān)系,最終檢測出節(jié)點(diǎn)是否異常。仿真結(jié)果表明,與傳統(tǒng)的信譽(yù)機(jī)制的閾值法進(jìn)行比較,改進(jìn)方法可以有效地檢測出節(jié)點(diǎn)節(jié)點(diǎn)入侵狀況,為網(wǎng)絡(luò)數(shù)據(jù)的安全使用提供了支持。
[Abstract]:Accurate detection of wireless network transmission data node intrusion can ensure the safe operation of wireless network. When the intrusion detection is carried out, the node behavior should be quantified and compared with the reputation value of the network node by using the pre-set reputation threshold to complete the detection. However, the traditional method analyzes the spontaneous critical state of variables in a certain area of wireless network, and finds out the most vulnerable network nodes to complete intrusion detection. However, it is impossible to compare the creditworthiness threshold of the node to be detected with the reputation value of the normal network node, which reduces the accuracy of intrusion detection. A wireless network transmission data node intrusion detection method based on reputation mechanism is proposed. Firstly, the behavior attributes of wireless network nodes are analyzed. After quantizing the data such as packet loss rate and transfer rate of nodes, a model is built to analyze the different attributes of different nodes. Then, the different attribute nodes are analyzed and judged by the established reputation mechanism, then the behavior of the nodes is quantified and compared with the reputation value of the network node by the pre-set reputation threshold. The relationship between the reputation value of the detected node and the pre-set threshold is determined, and whether the node is abnormal or not is detected. The simulation results show that compared with the traditional threshold method of reputation mechanism, the improved method can effectively detect the node intrusion status and provide support for the safe use of network data.
【作者單位】： 武漢科技大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;華中科技大學(xué)計(jì)算機(jī)學(xué)院;
【分類號】：TN915.08

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