网络攻击节点路径高效检测模型仿真研究

发布时间：2018-04-10 01:28

本文选题：网络攻击　切入点：节点路径检测　出处：《计算机仿真》2017年08期

【摘要】：网络攻击节点路径的高效检测,可以有效的提高网络的安全系数。对网络攻击节点路径的检测,需要通过计算入侵意图被实现的概率,获取入侵的最大可能性,完成对网络攻击的检测。传统方法描述不同攻击类型存在的关联性,获取引起网络系统状态变迁的渗透序列,但无法对入侵的可能性进行估计,导致检测精度低。提出基于攻击图的网络攻击节点路径检测模型。根据攻击图理论定义网络攻击图,检测出当前时刻攻击者的权限状态,获取网络的连接矩阵,得到攻击者所利用漏洞、攻击效果、攻击前提的形式化描述,检测网络漏洞被入侵者成功利用的概率、入侵者获取权限的概率和其入侵意图被实现的概率,获取在网络各个攻击路径下其入侵的最大可能性,以检测的结果为依据组建网络攻击节点路径检测模型。实验结果表明,所提模型设计精确度高,有效地提高了网络安全分析效率。
[Abstract]:The efficient detection of the network attack node path can effectively improve the safety factor of the network.To detect the path of the network attack node, we need to calculate the probability of the intention of the intrusion, obtain the maximum possibility of the intrusion, and complete the detection of the network attack.The traditional methods describe the relevance of different attack types and obtain the osmotic sequences that lead to network system state transition. However, it is impossible to estimate the possibility of intrusion, which leads to low detection accuracy.A network attack node path detection model based on attack graph is proposed.According to the theory of attack graph, the network attack graph is defined, the privilege state of the attacker is detected at the present time, the connection matrix of the network is obtained, and the formal description of the vulnerability, the attack effect and the premise of the attack are obtained.Detecting the probability of the network vulnerability being exploited successfully by the intruder, the probability that the intruder acquires the authority and the probability of its intrusion intention being realized, and obtaining the maximum possibility of its intrusion under each attack path of the network.Based on the results of the detection, a network attack node path detection model is established.The experimental results show that the proposed model is highly accurate and can effectively improve the efficiency of network security analysis.
【作者单位】：全球能源互联网研究院;
【基金】：国家电网公司科技项目(SGFJXTOOYJJS1600064)
【分类号】：TP393.08

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