# Cyber-Security in Smart Grid: Survey and Challenges

arXiv: https://arxiv.org/abs/1809.02609 · HTML: https://mlsanity.com/paper/1809.02609/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2018.

## Built on by (most cited first, 13 of 13)

- [False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey](https://mlsanity.com/paper/2111.14251/) (2021)
- [Constraints Satisfiability Driven Reinforcement Learning for Autonomous Cyber Defense](https://mlsanity.com/paper/2104.08994/) (2021)
- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)
- [Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey](https://mlsanity.com/paper/2101.08013/) (2021)
- [A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions](https://mlsanity.com/paper/2207.07738/) (2022)
- [The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey](https://mlsanity.com/paper/2401.05896/) (2024)
- [Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential Solutions for Fake-Normal Attacks: A Short Review](https://mlsanity.com/paper/2202.07050/) (2022)
- [A Hierarchical Terminal Recognition Approach based on Network Traffic Analysis](https://mlsanity.com/paper/2204.07726/) (2022)
- [Cascading Failures in Smart Grids under Random, Targeted and Adaptive Attacks](https://mlsanity.com/paper/2206.12735/) (2022)
- [A Systematic Review of Security Vulnerabilities in Smart Home Devices and Mitigation Techniques](https://mlsanity.com/paper/2507.01018/) (2025)
- [Real-Time Defense Against Coordinated Cyber-Physical Attacks: A Robust Constrained Reinforcement Learning Approach](https://mlsanity.com/paper/2509.10999/) (2025)
- [An AI-Enabled Hybrid Cyber-Physical Framework for Adaptive Control in Smart Grids](https://mlsanity.com/paper/2511.21590/) (2025)
- [Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems](https://mlsanity.com/paper/2601.00873/) (2025)

## Abstract (arXiv)

Smart grid uses the power of information technology to intelligently deliver energy to customers by using a two-way communication, and wisely meet the environmental requirements by facilitating the integration of green technologies. Although smart grid addresses several problems of the traditional grid, it faces a number of security challenges. Because communication has been incorporated into the electrical power with its inherent weaknesses, it has exposed the system to numerous risks. Several research papers have discussed these problems. However, most of them classified attacks based on confidentiality, integrity, and availability, and they excluded attacks which compromise other security criteria such as accountability. In addition, the existed security countermeasures focus on countering some specific attacks or protecting some specific components, but there is no global approach which combines these solutions to secure the entire system. The purpose of this paper is to provide a comprehensive overview of the relevant published works. First, we review the security requirements. Then, we investigate in depth a number of important cyber-attacks in smart grid to diagnose the potential vulnerabilities along with their impact. In addition, we proposed a cyber security strategy as a solution to address breaches, counter attacks, and deploy appropriate countermeasures. Finally, we provide some future research directions.