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1.湖南大学电气与信息工程学院,湖南省长沙市 410082
2.江苏省信息安全测评中心,江苏省无锡市 214072
3.国网江苏省电力有限公司电力科学研究院,江苏省南京市 210013
Received:28 December 2021,
Revised:2022-08-01,
Published Online:14 December 2022,
Published:25 January 2023
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王文博,刘绚,张博等.基于协议特征的电力工控网络流量异常行为检测方法[J].电力系统自动化,2023,47(2):137-145.
WANG Wenbo,LIU Xuan,ZHANG Bo,et al.Protocol Characteristics Based Detection Method for Abnormal Flow Behavior in Electric Power Industrial Control Network[J].Automation of Electric Power Systems,2023,47(2):137-145.
王文博,刘绚,张博等.基于协议特征的电力工控网络流量异常行为检测方法[J].电力系统自动化,2023,47(2):137-145. DOI: 10.7500/AEPS20211228006.
WANG Wenbo,LIU Xuan,ZHANG Bo,et al.Protocol Characteristics Based Detection Method for Abnormal Flow Behavior in Electric Power Industrial Control Network[J].Automation of Electric Power Systems,2023,47(2):137-145. DOI: 10.7500/AEPS20211228006.
随着信息通信技术在电力工控系统中的广泛应用,电力工控系统遭受网络攻击的风险不断增加。电力工控系统的信息传输和交互以通信协议的流量数据为载体,流量数据的应用层报文在传输过程中存在被窃取及篡改等风险。文中以IEC 60870-5-104协议为例,在对其脆弱性分析的基础上提出了基于协议特征的电力工控流量异常行为检测方法。首先,对电力工控流量进行应用层报文的提取及解析,并结合报文字段特征以及典型电力业务特征建立起电力工控流量正常行为模型。其次,依据正常行为模型对流量数据进行单字段畸形校验、多字段耦合逻辑校验、帧与帧时序逻辑校验、帧与帧上下文异常校验,实现流量异常行为的识别。最后,基于某220 kV变电站的实际流量数据集进行仿真,结果表明所提方法对于典型异常行为检测准确率约为99.98%,能够有效辨识电力工控系统流量异常行为,提升电力系统的安全性。
With the wide application of information communication technology in the power industrial control system
the risk of power industrial control system being attacked by network is increasing. The information transmission and interaction of the power industrial control system is carried by the flow data of the communication protocol. The application layer message of the industrial data has the risk of being stolen and tampered in the transmission process. Taking IEC 60870-5-104 protocol as an example
this paper proposes a protocol characteristics based method to detect the abnormal behavior of power industrial control flow on the basis of its vulnerability analysis. Firstly
the application layer message of power industrial control flow is extracted and analyzed
and the normal behavior model of power industrial control flow is established based on the message field characteristics and typical power business characteristics. Secondly
according to the normal behavior model
single field anomaly verification
multiple field coupling logic verification
frame to frame timing logic verification
and frame to frame context exception verification are performed on the flow data to identify abnormal flow behaviors. Finally
based on the actual flow data set of a 220 kV substation
the simulation results show that the accuracy of the proposed method for typical abnormal behavior detection is about 99.98%
which can effectively identify the abnormal flow behavior of power industrial control system and improve the security of power system.
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