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计算机系统应用:2018,27(8):203-208
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融合本体理论的电网动态威胁数据模型与可视感知
梁晶亮, 黄军胜, 白树军, 王鹏, 李睿
(贵州省遵义市供电局 信息中心, 遵义 563000)
Dynamic Threat Data Model and Visual Perception of Power Grid Fused Ontology Theory
LIANG Jing-Liang, HUANG Jun-Sheng, BAI Shu-Jun, WANG Peng, LI Rui
(Information Center, Zunyi Power Supply Bureau, Guizhou Province, Guizhou 563000, China)
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投稿时间:2017-12-13    修订日期:2018-01-04
中文摘要: 网络安全威胁可视化,深度融合网络状态和攻击形式,将网络中安全态势感知与可视化技术结合,实现全域网络可信状态下受到威胁的可视化表征.电力数据网络威胁可视化技术仍存在传统数据模型的表征能力受限以及状态特征冗余和离散导致表达可用程度低的问题.本文提出了融合本体理论与态势演变的电网动态威胁网格化可视感知,通过设计实体化的三阶段统一攻击威胁行为模型,有效解决了电力数据网络安全特征表征模糊问题.设计基于本体特征的深度内容检测方法,形成电网安全数据的紧密关系特征集,从而降低了状态特征的冗余程度,精细化处理后的网络威胁数据将通过态势阶梯,实现攻击行为的图形表征平滑渐变.通过贵州省遵义市供电局电网威胁可视化实验,验证本文方法提升网络安全威胁监测错误4%.
Abstract:Visualization of network security threats, deep integration of network status, and attack patterns, combine the security situational awareness and visualization technology, can realize the visualization representation of the global network under the trusted state. The technologies of network threat visualization still have the problem of limited representation ability of traditional data models, and the low availability of state feature redundancy and dispersion. In this study, dynamic threat data model and visual perception are proposed, which combines the ontology theory and the situation evolution. Unified attack behavior model with three solid states can help improve the problem of unclear description of network security characteristics. By designing deep content detection based on ontology features, the tight relationship characteristics set reduces the redundancy of security features. The refined network threat data will pass through the situation ladder to achieve the smooth gradient of the graphical representation of the attack behavior. The tests on Zunyi Power Supply Bureau of Guizhou Province verify the proposed method to improve the network security threat monitoring error by 4%.
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基金项目:遵义供电局网络威胁主动发现和预警研究项目(0603002017030102XX00001)
引用文本:
梁晶亮,黄军胜,白树军,王鹏,李睿.融合本体理论的电网动态威胁数据模型与可视感知.计算机系统应用,2018,27(8):203-208
LIANG Jing-Liang,HUANG Jun-Sheng,BAI Shu-Jun,WANG Peng,LI Rui.Dynamic Threat Data Model and Visual Perception of Power Grid Fused Ontology Theory.COMPUTER SYSTEMS APPLICATIONS,2018,27(8):203-208

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