信息安全分析任务中的网络攻击特征

V. Lakhno, Borys Husiev, A. Blozva, D. Kasatkin, T.Yu. Osypova
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引用次数: 2

摘要

本文提出了一种用于入侵检测系统的具有自学习元素的算法,以及一种由信息安全事件数据系统记录的改进聚类技术。所提出的方法不同于已知的使用熵方法的方法,熵方法允许数据作为同质组呈现,而且,每个这样的组(或簇)可能对应于预定的参数。提出的解决方案涉及评估描述所分析的入侵类别的集群之间动态依赖关系的可能性。研究发现,当信息安全事件出现新的迹象时,相应的尺度发生变化,描述了集群之间的距离。通过计算实验验证了所提方案的可操作性和充分性。在计算实验中发现,逐步计算网络攻击的信息特征参数,可以形成具有特征属性的数据的足够信息的聚类结构。这些属性进一步成为智能网络攻击检测系统知识库的基础。计算集群之间的动态依赖关系,允许对许多信息安全事件进行足够准确的定义,这些事件可以成为攻击检测系统检测到的进一步自动评估当前威胁程度的源数据。本文提出的聚类网络攻击迹象的方法和算法,在软件实现上比现有的类似方法更简单。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CLUSTERING NETWORK ATTACK FEATURES IN INFORMATION SECURITY ANALYSIS TASKS
The paper proposes an algorithm with self-learning elements for intrusion detection systems, as well as an improved clustering technique which is recorded by the data system concerning information security events. The proposed approaches differ from those known using an entropy approach allowing data to be presented as homogeneous groups, moreover, each such group (or cluster) may correspond to predetermined parameters. The proposed solutions relate to the possibilities of assessing dynamic dependencies between clusters characterizing the analysed classes of invasions. The studies have found that in case of manifestation of new signs of information security events, the corresponding scale changes and describes the distances between clusters. A computational experiment was conducted to verify the operability and adequacy of the proposed solutions. During the computational experiment, it has been found that step-by-step calculation of parameters of informative characteristics of network attacks allows to form sufficiently informative cluster structures of data having characteristic attributes. These attributes further become the basis for the knowledge base of intelligent network attack detection systems. Dynamic dependencies between clusters are calculated allowing for a sufficiently accurate definition of the many information security events that can become the source data for further automatic assessment of current threats extent detected by attack detection systems. The methodology and algorithm presented in the paper for clustering the signs of network attacks, in our opinion it is simpler for software implementation than existing analogues.
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