A Review of IDS Research in Smart Grid AMI Field

doi:10.3969/j.issn.1671-1122.2023.01.001

Abstract

Abstract:

As a key component of smart grid, advanced metering infrastructure (AMI) effectively supports important links such as real-time interactive distributed energy generation and storage in smart grid. However, the access of the network in AMI also puts the smart grid at severe security risks. In the field of AMI security, intrusion detection system (IDS) is widely used in AMI security protection due to its ability to actively detect attacks. By introducing the architecture of AMI, a key component of smart grid, this paper analyzed and determined the security weak points of AMI facing abnormal access. On this basis, this paper investigated the research status of connection-oriented and device-oriented IDS, detailed the application and development of IDS in the field of AMI in recent years, summarized and analyzed the problems that still exist in the field of IDS in the field of AMI, and gave a hierarchy outlook.

Key words: smart grid, advanced metering infrastructure, intrusion detection systems, cyber security

CLC Number:

TP309

JIN Zhigang, LIU Kai, WU Xiaodong. A Review of IDS Research in Smart Grid AMI Field[J]. Netinfo Security, 2023, 23(1): 1-8.

Figures/Tables 4

References 27

[1]	LIU Jianan, WENG Jian. Survey on Smart Grid Security[J]. Netinfo Security, 2016, 16(5): 78-84.
	刘家男, 翁健. 智能电网安全研究综述[J]. 信息网络安全, 2016, 16(5): 78-84.
[2]	CHEN Jiaqi, WANG Qi, TANG Yi, et al. Anomaly Detection Method for Cyber Physical Power System Considering Bilateral Features[J]. Power System Technology, 2022, 46(6): 2339-2348.
	陈家琪, 王琦, 汤奕, 等. 考虑双侧特征的电力信息物理系统异常检测方法[J]. 电网技术, 2022, 46(6):2339-2348.
[3]	BASTY F B A, LUO Dehan. Development and Challenges of Cyber-Security for Smart Grids[J]. Electrotechnics Electric, 2019(7): 5-8, 54.
	巴斯替, 骆德汉. 智能电网网络安全的发展与挑战[J]. 电工电气, 2019(7):5-8, 54.
[4]	FAISAL M A, AUNG Z, WILLIAMS J R, et al. Data-Stream-Based Intrusion Detection System for Advanced Metering Infrastructure in Smart Grid: A Feasibility Study[J]. IEEE Systems Journal, 2014, 9(1): 31-44. doi: 10.1109/JSYST.2013.2294120 URL
[5]	MIRZAEE P H, SHOJAFAR M, CRUICKSHANK H, et al. Smart Grid Security and Privacy: from Conventional to Machine Learning Issues (Threats and Countermeasures)[J]. IEEE Access, 2022, 10: 52922-52954. doi: 10.1109/ACCESS.2022.3174259 URL
[6]	LEON J P A, RICO-NOVELLA F J, LLOPIS L J D. Predictive Traffic Control and Differentiation on Smart Grid Neighborhood Area Networks[J]. IEEE Access, 2020, 8: 216805-216821. doi: 10.1109/ACCESS.2020.3041690 URL
[7]	GHOSAL A, CONTI M. Key Management Systems for Smart Grid Advanced Metering Infrastructure: A Survey[J]. IEEE Communications Surveys & Tutorials, 2019, 21(3): 2831-2848.
[8]	LIN Zhenzhi, CUI Xueyuan, JIN Weichao, et al. Key Technologies of Electricity Theft Detection at Consumer Side[J]. Automation of Electric Power Systems, 2022, 46(5): 188-199.
	林振智, 崔雪原, 金伟超, 等. 用户侧窃电检测关键技术[J]. 电力系统自动化, 2022, 46(5):188-199.
[9]	SAXENA N, CHOI B J, GRIJALVA S. Secure and Privacy-Preserving Concentration of Metering Data in AMI Networks[C]// IEEE. 2017 IEEE International Conference on Communications (ICC). Paris:IEEE, 2017: 1-7.
[10]	NASEER H, BHUTTA M N M, ALOJAIL M A. A Key Transport Protocol for Advance Metering Infrastructure (AMI) Based on Public Key Cryptography[C]//NCCS. 2020 International Conference on Cyber Warfare and Security (ICCWS). New York:IEEE, 2020: 1-5.
[11]	PASETTI M, FERRARI P, BELLAGENTE P, et al. Artificial Neural Network-Based Stealth Attack on Battery Energy Storage Systems[J]. IEEE Transactions on Smart Grid, 2021, 12(6): 5310-5321. doi: 10.1109/TSG.2021.3102833 URL
[12]	ANGELOS E W S, SAAVEDRA O R, CORTES O A C, et al. Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems[J]. IEEE Transactions on Power Delivery, 2011, 26(4): 2436-2442. doi: 10.1109/TPWRD.2011.2161621 URL
[13]	YAN Zhongzong, HE Wen. Electricity Theft Detection Base on Extreme Gradient Boosting in AMI[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-9.
[14]	JOKAR P, ARIANPOO N, LEUNG V C M. Electricity Theft Detection in AMI Using Customers’ Consumption Patterns[J]. IEEE Transactions on Smart Grid, 2015, 7(1): 216-226. doi: 10.1109/TSG.2015.2425222 URL
[15]	MAAMAR A, BENAHMED K. A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network[J]. Computers Materials & Continua(CMC), 2019, 60(1): 15-39.
[16]	PARK C H, KIM T. Energy Theft Detection in Advanced Metering Infrastructure Based on Anomaly Pattern Detection[J]. Energies, 2020, 13(15): 3832-3841. doi: 10.3390/en13153832 URL
[17]	YIP S C, TAN W N, TAN C K, et al. An Anomaly Detection Framework for Identifying Energy Theft and Defective Meters in Smart Grids[J]. International Journal of Electrical Power & Energy Systems, 2018, 101: 189-203. doi: 10.1016/j.ijepes.2018.03.025 URL
[18]	FENZA G, GALLO M, LOIA V. Drift-Aware Methodology for Anomaly Detection in Smart Grid[J]. IEEE Access, 2019, 7: 9645-9657. doi: 10.1109/ACCESS.2019.2891315
[19]	SUN C C, CARDENAS D J S, HAHN A, et al. Intrusion Detection for Cybersecurity of Smart Meters[J]. IEEE Transactions on Smart Grid, 2020, 12(1): 612-622. doi: 10.1109/TSG.2020.3010230 URL
[20]	LU Guanyu, TIAN Xiuxia. An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM[EB/OL]. (2021-04-01)[2022-08-11]. https://www.researchgate.net/publication/351052596_An_Efficient_Communication_Intrusion_Detection_Scheme_in_AMI_Combining_Feature_Dimensionality_Reduction_ and_Improved_LSTM.
[21]	ROSE T, KIFAYAT K, ABBAS S, et al. A Hybrid Anomaly-Based Intrusion Detection System to Improve Time Complexity in the Internet of Energy Environment[J]. Journal of Parallel and Distributed Computing, 2020, 145: 124-139. doi: 10.1016/j.jpdc.2020.06.012 URL
[22]	LIU Feifei, WU Zhongdong, DING Longbin, et al. Intrusion Detection Algorithm for AMI Based on Improved Online Sequential Extreme Learning Machine[J]. Computer Engineering, 2020, 46(9): 136-142, 148.
	刘菲菲, 伍忠东, 丁龙斌, 等. 基于改进在线序列极限学习机的AMI入侵检测算法[J]. 计算机工程, 2020, 46(9):136-142,148.
[23]	KHAN S, KIFAYAT K, KASHIF B A, et al. Intelligent Intrusion Detection System in Smart Grid Using Computational Intelligence and Machine Learning[J]. Transactions on Emerging Telecommunications Technologies, 2021, 32(6): 1-24.
[24]	SONG Chunhe, SUN Yingying, HAN Guangjie, et al. Intrusion Detection Based on Hybrid Classifiers for Smart Grid[EB/OL]. (2021-07-01)[2022-08-11]. https://www.researchgate.net/publication/351655230_Intrusion_detection_based_on_hybrid_classifiers_for_smart_grid.
[25]	YAO Ruizhe, WANG Ning, LIU Zhihui, et al. Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach[J]. Sensors, 2021, 21(2): 626-642. doi: 10.3390/s21020626 URL
[26]	MASSAFERRO P, MARTINO J M D, FERNANDEZ A. Fraud Detection on Power Grids While Transitioning to Smart Meters by Leveraging Multi-Resolution Consumption Data[J]. IEEE Transactions on Smart Grid, 2022, 13(3): 2381-2389. doi: 10.1109/TSG.2022.3148817 URL
[27]	ZHOU Tailin, XIAHOU Kaishun, ZHANG Luliang, et al. Real-Time Detection of Cyber-Physical False Data Injection Attacks on Power Systems[J]. IEEE Transactions on Industrial Informatics, 2020, 17(10): 6810-6819. doi: 10.1109/TII.2020.3048386 URL

面向层面	参考文献	发表年份	使用技术	评价
面向设备	参考文献[13]	2021	一种基于XGBoost的AMI窃电检测方案	模型在训练集不平衡时性能表现良好
	参考文献[16]	2020	一种基于异常的IDS，该模型只需要正常的能耗数据检验模型	该方法能有效预测异常能源消耗天数
	参考文献[15]	2019	基于K-means和DNN算法的IDS	注意到以往研究忽略了环境因素对IDS检测结果的影响，并提出解决办法
	参考文献[18]	2019	基于长短期记忆网络，提出一种应对概念漂移的异常检测方法	注意到消费者消费习惯的变化会造成IDS的假阳性率
	参考文献[19]	2021	SVM 和时间故障传播图	提出一个两阶段的网络入侵保护系统
	参考文献[17]	2018	基于线性规划的异常检测框架	有效检测出欺诈性消费者或有缺陷的智能电表
面向连接	参考文献[20]	2021	结合特征降维和改进的LSTM的入侵检测系统	有效降低由数据不平衡导致的高FAR
	参考文献[21]	2020	结合了K-means算法和SVM	有效降低了模型的检测时间
	参考文献[22]	2020	提出DBN-OS-RKELM算法，加入核函数与正则化	应用OS-ELM对实时入侵数据以块的方式进行在线更新分类以缩短检测时间
	参考文献[23]	2021	一种特征选择方法，并使用加权粒子群算法来改进	实验表明该方法在随机森林和神经网络上训练时间和FAR表现较好
	参考文献[24]	2021	结合LSTM与极端梯度提升算法	实验性能良好，召回率超过95%
	参考文献[25]	2021	基于CNN和LSTM的跨层特征融合的AMI入侵检测模型	在KDD99数据集上表现良好