A survey of intruder detection in smart grid systems: comparative analysis of rule-based, machine learning and deep learning

被引：0

作者：

Shehzadi, Kiran ^{[1
,2
]}

Abbas, Touqeer ^{[1
]}

Zainab, Ayesha ^{[2
]}

Li, Hui ^{[1
,2
]}

机构：

[1] Department of Computer Science and Technology, Beijing University of Chemical Technology, Beijing

[2] Department of Computer Science, Institute of Engineering and Technology (NFC-IET), Multan

来源：

International Journal of Security and Networks | 2025年 / 20卷 / 02期

关键词：

deep learning; detection; IDS; intrusions; smart grid; smart grid security;

D O I：

10.1504/IJSN.2025.146763

中图分类号：

学科分类号：

摘要：

The increasing sophistication of smart grids as cyber-physical systems renders them increasingly susceptible to cyberattacks. Attack detection has potential to be enhanced by implementing deep learning-based IDS methods. Deep learning-based IDS can improve attack detection in smart grids. However, challenges arise in managing vast data volumes generated across multiple communication levels when integrating deep learning into these systems. These issues are intensified by the exertion of detecting to illegal access, particularly in SCADA systems that use multi-tier protocols such as DNP3. Furthermore, the rapid growth of IoT in smart grids increases data processing demands, challenging DL model scalability and IDS integration due to standardisation and communication compatibility issues. Organisations struggle with resource allocation for employee training, technology updates, and security maintenance. Addressing these challenges requires goal-oriented strategies. This article explores enhancing smart grid flexibility through security upgrades, strategic planning, and regulatory compliance for effective IDS integration. Copyright © 2025 Inderscience Enterprises Ltd.

引用

页码：67 / 92

页数：25

共 164 条

[1]

Abadeh M.S., Habibi J., Aliari S., Using a particle swarm optimization approach for evolutionary fuzzy rule learning: a case study of intrusion detection, Information Processing and Management of Uncertainty in Knowledge Based Systems (IPMU), (2006)

[2]

Abbas T., Javed U., Mehmood F., Raza M., Li H., ChemGenX: AI in the chemistry classroom, Proceedings of the 2024 International Symposium on Artificial Intelligence for Education, (2024)

[3]

Abbas T., Razzaq A., Zia M.A., Mumtaz I., Saleem M.A., Akbar W., Khan M.A., Akhtar G., Shivachi C.S., Deep neural networks for automatic flower species localization and recognition, Computational Intelligence and Neuroscience, 2022, 1, (2022)

[4]

Abdulaal M.J., Ibrahem M.I., Mahmoud M.M., Khalid J., Aljohani A.J., Milyani A.H., Abusorrah A.M., Real-time detection of false readings in smart grid AMI using deep and ensemble learning, IEEE Access, 10, pp. 47541-47556, (2022)

[5]

Abdulganiyu O.H., Tchakoucht T.A., Saheed Y.K., A systematic literature review for network intrusion detection system (IDS), International Journal of Information Security, 22, 5, pp. 1125-1162, (2023)

[6]

Abou El Houda Z., Brik B., Khoukhi L., Ensemble learning for intrusion detection in SDN-based zero touch smart grid systems, 2022 IEEE 47th Conference on Local Computer Networks (LCN), (2022)

[7]

Acosta M.R.C., Ahmed S., Garcia C.E., Koo I., Extremely randomized trees-based scheme for stealthy cyber-attack detection in smart grid networks, IEEE Access, 8, pp. 19921-19933, (2020)

[8]

Ahmad Z., Khan A., Shiang W., Abdullah J., Ahmad F., Network intrusion detection system: a systematic study of machine learning and deep learning approaches, Transactions on Emerging Telecommunications Technologies, 32, 1, (2021)

[9]

Alani M.M., Mauri L., Damiani E., A two-stage cyber attack detection and classification system for smart grids, Internet of Things, 24, (2023)

[10]

Alavikia Z., Shabro M., A comprehensive layered approach for implementing internet of things-enabled smart grid: a survey, Digital Communications and Networks, 8, 3, pp. 388-410, (2022)

← 1 2 3 4 5 6 7 8 9 10 →