Cellular automata model based power network attack prediction technology

Lijuan YE; Yiting WANG; Licheng ZHU

doi:10.11959/j.issn.1000-0801.2023099

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Cellular automata model based power network attack prediction technology

Engineering and Application | 更新时间：2024-06-05

- Cellular automata model based power network attack prediction technology
- Telecommunications Science Vol. 39, Issue 4, Pages: 173-179(2023)
- 作者机构：
  
  1. 国网青海省电力公司，青海西宁 810008
  2. 上海柒志科技有限公司，上海 200122
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023099
  CLC： TP391
- Published Online：2023-04，
  
  Published：20 April 2023
- 稿件说明：
移动端阅览
Lijuan YE, Yiting WANG, Licheng ZHU. Cellular automata model based power network attack prediction technology[J]. Telecommunications science, 2023, 39(4): 173-179.
DOI：

Lijuan YE, Yiting WANG, Licheng ZHU. Cellular automata model based power network attack prediction technology[J]. Telecommunications science, 2023, 39(4): 173-179. DOI： 10.11959/j.issn.1000-0801.2023099.

摘要

传统电力网络攻击范围预测技术的预测范围不够广，导致电力网络安全性提升效果不明显。为此，提出基于细胞自动机模型的电力网络攻击预测技术。搭建电力细胞自动机模型，将细胞自动机中的细胞看作电力细胞，建立细胞活力值转换规则，将其与攻击者执行攻击概率相结合，预测电力细胞的发展变化。根据中心电力细胞及邻域电力细胞的变化趋势预测电力网络攻击。实验结果表明：在时间因素影响下，提出的基于细胞自动机模型电力网络攻击预测技术的预测攻击节点位置与原始节点基本一致，预测后负荷切除量始终在100 MW以下，本文所提技术的有效性更好。

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.

关键词

Keywords

references

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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