基于函数挖掘的能源信息物理系统数据安全风险识别算法

doi:10.11930/j.issn.1004-9649.202007135

中国电力 ›› 2021, Vol. 54 ›› Issue (3): 23-30,37.DOI: 10.11930/j.issn.1004-9649.202007135

• 国家“十三五”智能电网重大专项专栏：（六）先进计算与人工智能技术专栏 • 上一篇下一篇

基于函数挖掘的能源信息物理系统数据安全风险识别算法

邓松¹, 蔡清媛¹, 高昆仑^2,3, 张建堂¹, 饶玮^2,3, 朱力鹏^2,3

1. 南京邮电大学先进技术研究院，江苏南京 210023;
2. 全球能源互联网研究院有限公司，北京 102209;
3. 电力系统人工智能(联研院)国家电网公司联合实验室，北京 102209

收稿日期:2020-07-27 修回日期:2021-01-18 出版日期:2021-03-05 发布日期:2021-03-17
作者简介:邓松(1980-)，男，博士，副研究员，从事电网信息安全与防护，电力大数据及数据挖掘研究，E-mail：ds16090311@163.com;蔡清媛(1997-)，女，硕士研究生，从事电网信息安全与防护、电力大数据及数据挖掘研究，E-mail：dmccxysc@163.com;高昆仑(1972-)，男，博士，高级工程师(教授级)，从事电力系统自动化与信息化技术研究，E-mail：gkl@geiri.sgcc.com.cn
基金资助:
国家自然科学基金资助项目(网络攻击下能源互联网数据容侵评估及可靠存储机制研究，51977113；面向有源配电网的数据传输优化及智能过滤机制，51507084)

Data Security Risk Recognition Algorithm for Energy Cyber Physics System Based on Function Mining

DENG Song¹, CAI Qingyuan¹, GAO Kunlun^2,3, ZHANG Jiantang¹, RAO Wei^2,3, ZHU Lipeng^2,3

1. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
2. Global Energy Interconnection Research Institute Co., Ltd., Beijing 102209, China;
3. Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory (GEIRI), Beijing 102209, China

Received:2020-07-27 Revised:2021-01-18 Online:2021-03-05 Published:2021-03-17
Supported by:
This work is supported by the National Natural Science Foundation of China (Data Tolerance Intrusion Assessment and Reliable Storage for Energy Internet under Cyber Attacks, No.51977113, Data Transmission Optimization and Intelligent Filtering Mechanism for Active Distribution Network, No.51507084)

摘要/Abstract

摘要： 数据安全风险评估对于能源信息物理系统安全稳定运行至关重要。现有的从二次设备、信息等角度来分析数据安全风险已经无法满足能源信息物理系统广泛的能源接入和各能源之间的能量、信息交互需求。首先提出基于粗糙集的数据安全风险要素特征选择算法，对影响能源信息物理系统中数据的安全风险特征集进行特征选择，降低能源信息物理系统数据安全风险要素集的维度；在此基础上，利用基因表达式编程（gene expression programming, GEP）的函数挖掘特性，提出基于混合GEP的能源信息物理系统数据安全风险识别算法，通过设计小生境种群生成策略以及动态自适应变异概率动态调整策略来提高数据安全风险识别的准确率和效率。仿真实验结果表明，所提算法对于复杂高维的能源信息物理系统数据安全风险集的识别和预测具有较高的准确率和较强的实用性，可为下一步制定能源信息物理系统数据安全防护策略提供理论方法支撑。

关键词: 基因表达式编程, 粗糙集, 特征选择, 风险识别, 能源信息物理系统

Abstract: Data security risk assessment is essential for the safe and stable operation of energy cyber physics system (CPS). The existing data security risk analysis from the perspective of secondary equipment and information cannot meet the requirements for extensive energy access as well as energy and information interaction between various energy sources in the energy CPS. Firstly, a feature selection algorithm for data security risk elements based on rough set (FSDSRF-RS) is proposed to select the data security risk feature sets in the energy CPS, consequently reducing the dimensions of the data security risk element sets of the energy CPS. And then, a data security risk recognition algorithm for energy cyber physics system based on hybrid gene expression programming (DSRR-HGEP) is proposed. In the DSRR-HGEP, a niche-based population generation strategy and a dynamic adaptive mutation probability adjustment strategy are designed to improve the accuracy and efficiency of data security risk identification. Simulation and experimental results show that the proposed algorithm in this paper has a high recognition and prediction accuracy for the complex and high-dimensional data security risk sets in the energy CPS, and can provide a theoretical support for formulating data security protection strategies of the energy cyber physical system in the future.

Key words: gene expression programming, rough set, feature selection, risk recognition, energy cyber physics system

邓松, 蔡清媛, 高昆仑, 张建堂, 饶玮, 朱力鹏. 基于函数挖掘的能源信息物理系统数据安全风险识别算法[J]. 中国电力, 2021, 54(3): 23-30,37.

DENG Song, CAI Qingyuan, GAO Kunlun, ZHANG Jiantang, RAO Wei, ZHU Lipeng. Data Security Risk Recognition Algorithm for Energy Cyber Physics System Based on Function Mining[J]. Electric Power, 2021, 54(3): 23-30,37.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202007135

https://www.electricpower.com.cn/CN/Y2021/V54/I3/23

参考文献

[1] 王继业, 郭经红, 曹军威, 等. 能源互联网信息通信关键技术综述[J]. 智能电网, 2015, 3(6): 473-485
WANG Jiye, GUO Jinghong, CAO Junwei, et al. Review on information and communication key technologies of energy Internet[J]. Smart Grid, 2015, 3(6): 473-485
[2] 袁智勇, 赵懿祺, 郭祚刚, 等. 面向能源互联网的综合能源系统规划研究综述[J]. 南方电网技术, 2019, 13(7): 1-9
YUAN Zhiyong, ZHAO Yiqi, GUO Zuogang, et al. Research summary of integrated energy systems planning for energy Internet[J]. Southern Power System Technology, 2019, 13(7): 1-9
[3] 王继业, 孟坤, 曹军威, 等. 能源互联网信息技术研究综述[J]. 计算机研究与发展, 2015, 52(5): 1109-1126
WANG Jiye, MENG Kun, CAO Junwei, et al. Information technology for energy Internet: a survey[J]. Journal of Computer Research and Development, 2015, 52(5): 1109-1126
[4] 韩丽芳, 胡博文, 杨军, 等. 基于攻击预测的电力CPS安全风险评估[J]. 中国电力, 2019, 52(1): 48-56
HAN Lifang, HU Bowen, YANG Jun, et al. A new security risk assessment method for cyber physical power system based on attack prediction[J]. Electric Power, 2019, 52(1): 48-56
[5] 毛子骏, 梅宏, 肖一鸣, 等. 基于贝叶斯网络的智慧城市信息安全风险评估研究[J]. 现代情报, 2020, 40(5): 19-26, 40
MAO Zijun, MEI Hong, XIAO Yiming, et al. Risk assessment of smart city information security based on Bayesian network[J]. Journal of Modern Information, 2020, 40(5): 19-26, 40
[6] 梁智强, 林丹生. 基于电力系统的信息安全风险评估机制研究[J]. 信息网络安全, 2017(4): 86-90
LIANG Zhiqiang, LIN Dansheng. Information security risk assessment mechanism research based on power system[J]. Netinfo Security, 2017(4): 86-90
[7] ZHOU H Z, YU G, LI L G. Cloud communication based ship communication network security risk assessment model[J]. Journal of Coastal Research, 2020, 95(S1): 991.
[8] 王丰, 张春平, 林瑜, 等. 军事院校信息系统安全风险的可拓识别评估[J]. 武汉理工大学学报(信息与管理工程版), 2018, 40(6): 606-609
WANG Feng, ZHANG Chunping, LIN Yu, et al. Extension identification assessment of information system security risk in military academies[J]. Journal of Wuhan University of Technology (Information & Management Engineering), 2018, 40(6): 606-609
[9] 王皓然, 严彬元. 依赖小波神经网络算法的信息安全风险评估方法[J]. 信息技术, 2018, 42(12): 93-96
WANG Haoran, YAN Binyuan. Information security risk assessment method based on wavelet neural network algorithm[J]. Information Technology, 2018, 42(12): 93-96
[10] 任贝贝. 一种风险评估和等级防护相结合的信息风险预测系统[J]. 计算机应用与软件, 2019, 36(2): 151-154
REN Beibei. An information risk prediction system combining risk assessment and level protection[J]. Computer Applications and Software, 2019, 36(2): 151-154
[11] 柴继文, 王胜, 梁晖辉, 等. 基于层次分析法的信息安全风险评估要素量化方法[J]. 重庆大学学报, 2017, 40(4): 44-53
CHAI Jiwen, WANG Sheng, LIANG Huihui, et al. An AHP-based quantified method of information security risk assessment elements[J]. Journal of Chongqing University, 2017, 40(4): 44-53
[12] YU J J, HU M, WANG P. Evaluation and reliability analysis of network security risk factors based on D-S evidence theory[J]. Journal of Intelligent & Fuzzy Systems, 2018, 34(2): 861-869.
[13] 许硕, 唐作其, 王鑫. 基于D-AHP与灰色理论的信息安全风险评估[J]. 计算机工程, 2019, 45(7): 194-202
XU Shuo, TANG Zuoqi, WANG Xin. Information security risk assessment based on D-AHP and grey theory[J]. Computer Engineering, 2019, 45(7): 194-202
[14] 詹雄, 郭昊, 何小芸, 等. 国家电网边缘计算信息系统安全风险评估方法研究[J]. 计算机科学, 2019, 46(增刊2): 428-432
ZHAN Xiong, GUO Hao, HE Xiaoyun, et al. Research on security risk assessment method of state grid edge computing information system[J]. Computer Science, 2019, 46(S2): 428-432
[15] 殷加玞, 赵冬梅. 基于全概率风险度量的电力系统备用风险评估方法[J]. 电力自动化设备, 2020, 40(1): 156-162
YIN Jiafu, ZHAO Dongmei. Reserve risk assessment method of power system based on total probability risk measure[J]. Electric Power Automation Equipment, 2020, 40(1): 156-162
[16] 戴胜华, 谢旭旭. 基于改进型AHP与证据理论的应答器系统风险评估[J]. 安全与环境学报, 2019, 19(1): 49-55
DAI Shenghua, XIE Xuxu. Risk assessment of balise system based on the improved AHP and evidence theory[J]. Journal of Safety and Environment, 2019, 19(1): 49-55
[17] 时召伟, 魏松杰. 基于贝叶斯网络的Android应用风险评估的研究[J]. 合肥工业大学学报(自然科学版), 2020, 43(6): 753-757
SHI Zhaowei, WEI Songjie. Research on Android application risk assessment based on Bayesian network[J]. Journal of Hefei University of Technology (Natural Science), 2020, 43(6): 753-757
[18] 赵刚, 吴天水. 结合灰色网络威胁分析的信息安全风险评估[J]. 清华大学学报(自然科学版), 2013, 53(12): 1761-1767
ZHAO Gang, WU Tianshui. Information security risk assessment based on G-ANP[J]. Journal of Tsinghua University (Science and Technology), 2013, 53(12): 1761-1767
[19] ALOHALI M, CLARKE N, FURNELL S. The design and evaluation of a user-centric information security risk assessment and response framework[J]. International Journal of Advanced Computer Science and Applications, 2018, 9(10): 148-163.
[20] 周超, 潘平, 黄亮. 基于量子门线路神经网络的信息安全风险评估[J]. 计算机工程, 2018, 44(12): 39-45
ZHOU Chao, PAN Ping, HUANG Liang. Risk assessment of information security based on quantum gate circuit neural networks[J]. Computer Engineering, 2018, 44(12): 39-45
[21] 董朝阳, 陈莹莹, 罗逢吉. 未来主动配电网中的新型数据驱动应用: 技术, 展望与挑战[J]. 电力建设, 2017, 38(5): 2-10
DONG Zhaoyang, CHEN Yingying, LUO Fengji. Innovative data-driven applications in future active distribution network: technologies, prospect and challenges[J]. Electric Power Construction, 2017, 38(5): 2-10
[22] 杨英仪. 面向能源互联网的数据一致性框架[J]. 广东电力, 2017, 30(12): 22-28
YANG Yingyi. A data consensus framework for energy Internet[J]. Guangdong Electric Power, 2017, 30(12): 22-28
[23] KHANNA K, PANIGRAHI B K, JOSHI A. Bi-level modelling of false data injection attacks on security constrained optimal power flow[J]. IET Generation, Transmission & Distribution, 2017, 11(14): 3586-3593.
[24] 王电钢, 黄林, 刘捷, 等. 考虑负荷虚假数据注入攻击的电力信息物理系统防御策略[J]. 电力系统保护与控制, 2019, 47(1): 28-34
WANG Diangang, HUANG Lin, LIU Jie, et al. Cyber-physical system defense strategy considering loaded false data injection attacks[J]. Power System Protection and Control, 2019, 47(1): 28-34
[25] YANG Q Y, LI D H, YU W, et al. Toward data integrity attacks against optimal power flow in smart grid[J]. IEEE Internet of Things Journal, 2017, 4(5): 1726-1738.
[26] LIU X, BAO Z, LU D, et al. Modeling of local false data injection attacks with reduced network information[J]. IEEE Transactions on Smart Grid, 2015, 6(4): 1686-1696.
[27] FERREIRA C. Gene expression programming: a new adaptive algorithm for solving problems[J]. Complex Systems, 2001, 13(2): 87-129.

基于函数挖掘的能源信息物理系统数据安全风险识别算法

Data Security Risk Recognition Algorithm for Energy Cyber Physics System Based on Function Mining

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics

[1]	陈娟, 惠东, 范茂松, 胡娟, 褚永金. 基于粗糙集的电池储能电站海量数据处理方法[J]. 中国电力, 2022, 55(2): 44-50,208.
[2]	田宇, 罗沙, 李宾宾, 孙文. 采用Fisher线性判别法提取GIS内部局部放电信号最优能量特征[J]. 中国电力, 2019, 52(9): 93-101.
[3]	吴丹岳. 基于直觉模糊粗糙集相似度的电压暂降源定位方法[J]. 中国电力, 2017, 50(3): 128-132.
[4]	高正中，龚群英，刘隆吉，李世光，赵丽娜. 基于BP网络算法优化粗糙-Petri网的电网故障诊断[J]. 中国电力, 2016, 49(8): 12-16.
[5]	杨锡运，任杰，肖运启. 基于粗糙集的光伏输出功率组合预测模型[J]. 中国电力, 2016, 49(12): 133-138.
[6]	赵慧材，陈跃辉，陈瑞先，彭子扬. 结合模糊粗糙集和支持向量机的电力负荷短期预测方法[J]. 中国电力, 2015, 48(2): 45-48.
[7]	李顺昕，岳云力，李佳莲，张兴平. 基于粗糙集和集成赋权的地市级电网运行评价体系[J]. 中国电力, 2015, 48(10): 123-126.
[8]	吕启深, 曾辉雄, 姚森敬, 黄荣辉, 张海龙. 基于贝叶斯网络和粗糙集约简的变压器故障诊断[J]. 中国电力, 2013, 46(9): 75-79.

模态框（Modal）标题

基于函数挖掘的能源信息物理系统数据安全风险识别算法

Data Security Risk Recognition Algorithm for Energy Cyber Physics System Based on Function Mining

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics