基于危险程度网络单个节点恶意程度评估模型

发布时间：2018-05-17 18:35

本文选题：神经网络 + 入侵检测　；参考：《计算机仿真》2013年09期

【摘要】：研究网络节点危险程度评估优化入侵检测问题。由于入侵的多样性和随机性,造成准确检测困难。传统的网络安全模型都是对信誉度或信任度等概念完成恶意节点整体检测,因为单个节点属性较为复杂,所承担的作用不同,使得针对单个节点信息评估过程较为粗糙,很难设定准确阀值进行精确判断,造成传统模型对单个节点危险程度评估不准。提出一种危险程度的网络节点恶意程度评估模型,使用马尔科夫算法与贝叶斯学习器计算单个节点的危险度,运用贝叶斯方法推断出节点恶意程度的解空间,依据节点的属性特征计算节点的恶意度,克服传统方法不能对单个节点做出判断的弊端。实验表明,与已有的安全模型相比,提出的安全管理模型对恶意节点具有更高的检测率。
[Abstract]:Research on network node risk assessment and optimization of intrusion detection problem. Due to the diversity and randomness of intrusion, it causes accurate detection difficulties. The evaluation process of the node information is relatively rough, it is difficult to set accurate thresholds for accurate judgment, which causes the traditional model to evaluate the risk degree of single node. A risk degree evaluation model of network node malware is proposed, and the Markoff algorithm and Bayesian classifier are used to calculate the risk degree of a single node, and the Bayesian formula is used. The method deduce the solution space of the node's malicious degree, calculate the malicious degree of the node according to the attribute characteristics of the node, overcome the disadvantage that the traditional method can not judge the single node. The experiment shows that the proposed security management model has a higher detection rate to the malicious node compared with the existing security model.
【作者单位】：贵州大学计算机科学与信息学院;
【分类号】：TP393.08

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