无线网络传输数据节点入侵优化检测仿真

发布时间：2019-03-22 14:06

【摘要】：对无线网络传输数据节点入侵进行准确检测,可保证无线网络的安全运行。进行入侵检测时,应将节点行为进行量化后运用预先设定的信誉阈值与网络节点的信誉值比较完成检测,但是传统方法通过对无线网络某一区域环境中变量自发性的临界变化状态进行分析,找出最脆弱的网络节点完成入侵检测,但是无法将待检测的节点信誉阈值与正常网络节点的信誉值进行比对,降低了入侵检测的精确度。提出一种基于信誉机制的无线网络传输数据节点入侵优化检测方法。首先,对无线网络节点的行为属性进行分析,通过对节点的丢包率、转发率等数据实现量化后构建模型,分析不同节点所具有的不同属性。然后,对不同属性节点通过建立的信誉机制进行分析判断,将节点行为进行量化后运用预先设定的信誉阈值与网络节点的信誉值比较,确定被检测节点信誉值与预先设定阈值的大小关系,最终检测出节点是否异常。仿真结果表明,与传统的信誉机制的阈值法进行比较,改进方法可以有效地检测出节点节点入侵状况,为网络数据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.
【作者单位】：武汉科技大学计算机科学与技术学院;华中科技大学计算机学院;
【分类号】：TN915.08

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