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

doi:10.11930/j.issn.1004-9649.202007135

Abstract

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

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.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202007135

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

References

[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.