基于细胞自动机模型电力网络攻击预测技术

doi:10.11959/j.issn.1000-0801.2023099

Abstract

Abstract:

The prediction range of the traditional power network attack range prediction technology was not wide enough, resulting in the poor effect of power network security improvement.For this reason, a power network attack prediction technology based on cellular automata model was proposed.A cellular automata model was built, regarding the cells in the cellular automata as power cells, a cell vitality value conversion rule was established and combined with the attack probability of attackers to predict the development and change of power cells.The power network attack was predicted according to the change trend of central power cells and neighboring power cells.The experimental result shows that, under the influence of time factors, the predicted attack node location of the proposed power network attack prediction technology based on cellular automata model is basically consistent with the original node, and the predicted load removal is always below 100 MW, the proposed technology is more effective.

Key words: cellular automata, power network, attack probability, network information, prediction model

CLC Number:

TP391

Lijuan YE, Yiting WANG, Licheng ZHU. Cellular automata model based power network attack prediction technology[J]. Telecommunications Science, 2023, 39(4): 173-179.

Figures/Tables 4

References 15

[1]	张红, 沈士根, 吴小军 ,等. 基于元胞自动机和静态贝叶斯博弈的 WSN 恶意程序传染模型[J]. 电信科学, 2019,35(6): 60-69.
	ZHANG H , SHEN S G , WU X J ,et al. WSN malware infection model based on cellular automaton and static Bayesian game[J]. Telecommunications Science, 2019,35(6): 60-69.
[2]	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 L F , HU B W , YANG J ,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.
[3]	刘巍, 李锰, 李秋燕 ,等. 基于改进遗传算法的电网投资组合预测方法[J]. 电力系统保护与控制, 2020,48(8): 78-85.
	LIU W , LI M , LI Q Y ,et al. Power grid portfolio forecasting method based on an improved genetic algorithm[J]. Power System Protection and Control, 2020,48(8): 78-85.
[4]	阳育德, 蓝水岚, 覃智君 ,等. 电力信息物理融合系统的网络-物理协同攻击[J]. 电力自动化设备, 2020,40(2): 97-102.
	YANG Y D , LAN S L , QIN Z J ,et al. Coordinated cyber-physical attacks of cyber-physical power system[J]. Electric Power Automation Equipment, 2020,40(2): 97-102.
[5]	陈碧云, 李弘斌, 李滨 . 伪量测建模与 AUKF 在配电网虚假数据注入攻击辨识中的应用[J]. 电网技术, 2019,43(9): 3226-3234.
	CHEN B Y , LI H B , LI B . Application research on pseudo measurement modeling and AUKF in FDIAs identification of distribution network[J]. Power System Technology, 2019,43(9): 3226-3234.
[6]	李昌祖, 牛东晓, 张欣岩 ,等. 基于改进的粒子群优化BP神经网络浙江电能替代潜力预测模型[J]. 科学技术与工程, 2020,20(13): 5173-5179.
	LI C Z , NIU D X , ZHANG X Y ,et al. Zhejiang Province electric power substitution potential prediction model based on improved particle swarm optimization BP neural network[J]. Science Technology and Engineering, 2020,20(13): 5173-5179.
[7]	刘晓琴, 王大志 . 基于溯因推理网络的电网故障预测方法研究[J]. 控制工程, 2019,26(2): 343-348.
	LIU X Q , WANG D Z . Research on power grid fault forecast based on abductive reasoning network[J]. Control Engineering of China, 2019,26(2): 343-348.
[8]	许言路, 武志锴, 朱赫炎 ,等. 基于多尺度卷积神经网络的短期电力负荷预测[J]. 沈阳工业大学学报, 2020,42(6): 618-623.
	XU Y L , WU Z K , ZHU H Y ,et al. Short-term load forecasting based on multi-scale convolutional neural network[J]. Journal of Shenyang University of Technology, 2020,42(6): 618-623.
[9]	王宇飞, 李俊娥, 刘艳丽 ,等. 容忍阶段性故障的协同网络攻击引发电网级联故障预警方法[J]. 电力系统自动化, 2021,45(3): 24-32.
	WANG Y F , LI J E , LIU Y L ,et al. Staged failure tolerance based early warning method for cascading failures in power grid caused by coordinated cyber attacks[J]. Automation of Electric Power Systems, 2021,45(3): 24-32.
[10]	韩林, 赵旭东, 陈志龙 ,等. 蓄意攻击下城市电力网络毁伤韧性评估[J]. 中国安全科学学报, 2021,31(6): 161-169.
	HAN L , ZHAO X D , CHEN Z L ,et al. Research on damage resilience assessment of urban power networks under intentional attacks[J]. China Safety Science Journal, 2021,31(6): 161-169.
[11]	孙平远, 刘科研, 齐冬莲 . 基于电力信息物理系统实时仿真平台的网络安全仿真[J]. 电力建设, 2020,41(2): 40-46.
	SUN P Y , LIU K Y , QI D L . Cyber security simulation based on real-time simulation platform of cyber physical power system[J]. Electric Power Construction, 2020,41(2): 40-46.
[12]	王珂, 田来, 吴俊 . 基于区域毁伤的基础设施网络抗毁性评估模型[J]. 系统工程与电子技术, 2020,42(5): 1102-1108.
	WANG K , TIAN L , WU J . Survivability assessing model of infrastructure networks based on regional damages[J]. Systems Engineering and Electronics, 2020,42(5): 1102-1108.
[13]	李永攀, 彭伟伦, 门锟 ,等. 基于多视角低秩分析的电力状态不良数据检测[J]. 电子科技大学学报, 2019,48(3): 361-365.
	LI Y P , PENG W L , MEN K ,et al. An approach for detecting band data in smart grid based on low-rank multi-view analysis[J]. Journal of University of Electronic Science and Technology of China, 2019,48(3): 361-365.
[14]	狄冲, 李桐 . 网络未知攻击检测的深度学习方法[J]. 计算机工程与应用, 2020,56(22): 109-116.
	DI C , LI T . Network unknown attack detection with deep learning[J]. Computer Engineering and Applications, 2020,56(22): 109-116.
[15]	董刚, 余伟, 玄光哲 . 高级持续性威胁中攻击特征的分析与检测[J]. 吉林大学学报(理学版), 2019,57(2): 339-344.
	DONG G , YU W , XUAN G Z . Analysis and detection of attack characteristics in advanced persistent threats[J]. Journal of Jilin University (Science Edition), 2019,57(2): 339-344.

Metrics

Recommended 0

No Suggested Reading articles found!

组件编号	组件信息	CVE编号	漏洞编号	漏洞说明	漏洞利用攻击编号
Z1	工程师站	CVE-2018-3951	D1	HTML代码执行	a1
Z2	Web服务器	CVE-2018-5056	D2	内核中的漏洞允许特权提升	a2
Z3	WinCC操作员站	CVE-2018-2846	D3	数据库SQL注入漏洞	a3
Z4	文件服务器	CVE-2018-4627	D4	组件权限许可和访问控制漏洞	a4
Z5	变频PLC控制器	CVE-2018-0671	D5	模块访问安全绕过漏洞	a5
Z6	气动阀PLC控制器	CVE-2018-0084	D6	远程缓冲区溢出	a6
Z7	数据服务器	CVE-2018-0076	D7	释放后重用漏洞	a7

攻击路径种类	文献[7]方法	文献[8]方法	本文所提方法
path1	376 MW	362 MW	72 MW
path2	232 MW	294 MW	56 MW
path3	352 MW	276 MW	97 MW
path4	328 MW	306 MW	54 MW

Cellular automata model based power network attack prediction technology

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 4

References 15

Related Articles 15

Metrics

Recommended 0

[1]	Lu HAN, Weiyu CHEN, Fei ZHANG, Jianfeng HE, Huaizhen SU. Big data classification method of non relational distributed submission information under differentiated requirements [J]. Telecommunications Science, 2023, 39(6): 114-121.
[2]	Yuhang JIA, Yixue LEI, Yipeng ZHANG, Yunfei ZHANG. Network information exposure based QoS prediction in tele-operated driving [J]. Telecommunications Science, 2023, 39(3): 153-161.
[3]	Tao HE, Zhendong YANG, Chang CAO, Yan ZHANG, Xiongyan TANG. Analysis of some key technical problems in the development of computing power network [J]. Telecommunications Science, 2022, 38(6): 62-70.
[4]	Qianying ZHAO, Wenjuan XING, Bo LEI, Lintao Jiang. A solution of computing power network based on domain name resolution [J]. Telecommunications Science, 2021, 37(10): 86-92.
[5]	Chang CAO, Shuai ZHANG, Ying LIU, Xiongyan TANG. Some key technical issues in the evolution from cloud network to computing network [J]. Telecommunications Science, 2021, 37(10): 93-101.
[6]	Xuan WEI,Ke RUAN,Xiaoying HUANG,Xun CHEN,Cancan HUANG. Automatic prediction for IP backbone network traffic [J]. Telecommunications Science, 2020, 36(8): 175-183.
[7]	Bo LEI,Jianglong WANG,Qianying ZHAO,Yongzhi YU,Mingchuan YANG. Novel network virtualization architecture based on the convergence of computing,storage and transport resources [J]. Telecommunications Science, 2020, 36(7): 42-54.
[8]	Chang CAO,Shuai ZHANG,Ying LIU,Xiongyan TANG. Convergence of telco cloud and bearer network based computing power network orchestration [J]. Telecommunications Science, 2020, 36(7): 55-62.
[9]	Daoyuan LIU,Keda SUN,Junliang ZHOU,Haidong FAN. Application of fuzzy comprehensive evaluation method in network information security assessment of electric power enterprises [J]. Telecommunications Science, 2020, 36(3): 34-41.
[10]	Bowei WANG,Jin TAN,Wenbin JIANG. Data aggregation scheme based on SDN for internet of things [J]. Telecommunications Science, 2019, 35(2): 27-34.
[11]	Kailu MENG,Keqiang YUE,Junna SHANG. Wireless sensor network nodes localization method based on cellular automata bat algorithm [J]. Telecommunications Science, 2017, 33(11): 56-65.
[12]	Shi PENG,Hao GUO,Tao WANG. G-O vulnerability forecasting model and its improvement based on loss [J]. Telecommunications Science, 2015, 31(Z1): 99-105.
[13]	Qin Yu,Zhihui Xiao,Ning An. AODV-ECA: Energy-Efficient AODV Routing Protocol Using Cellular Automata in Wireless Sensor Network [J]. Telecommunications Science, 2013, 29(6): 105-109.
[14]	Kai Sun,Tao Guo. Research and Application of the Structure and Quantity Prediction Model of Physical Channels Under Era of 3G [J]. Telecommunications Science, 2012, 28(4): 20-25.
[15]	Xiaoping Su,Yurong Song,Qin Shen,Jungang Lou. A Malware Propagation Model With GAF-Based Clustering in Wireless Sensor Network [J]. Telecommunications Science, 2011, 27(8): 33-38.