# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
###### Abstract  
The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers. Internet of Things; smart grid security; cyber-physical systems; cybersecurity; cyber warfare; counter-terrorism +
Footnote †: journal: _Future Internet_  
malware infections, unauthorized access, security breaches, Distributed Denial-of-Service (DDoS) attacks, and False Data Injection (FDI) attacks. These attacks can result in power outages, disruptions, or interruptions of vital infrastructure services, or even physical harm. As a result, robust security measures are essential to protect the infrastructure of any smart grid, including access controls, encryption, intrusion detection, risk assessments, vulnerability testing, and incident response plans, to effectively identify and mitigate these potential threats. Safety is another issue surrounding the smart grid critical infrastructure and requires mitigating risks associated with physical hazards, electrical accidents, aging equipment, and operational failures. Maintaining safety requires regular safety checks, constant updates, compliance, and thorough risk assessments. Therefore, prioritizing smart grid safety and security can ensure the reliability, resilience, and trustworthiness of renewable energy sources, facilitating the adoption of sustainable and efficient future electrical power systems. This is particularly true, given that the smart grid system represents a major technological advancement that addresses the challenges of updating the electrical grid and meeting the demands of an expanding population. The Multi-Layer Threat-Defense Alignment Framework (MLTDAF), presented in this article as a unique contribution, maps threats and corresponding defenses to each architectural layer of the smart grid, thereby bridging the gap between current attack categories and actual implementation. This framework offers a scalable and dynamic model to inform the design of security architectures and threat mitigation strategies. Traditional power grids have evolved into intelligent, data-driven infrastructures that enhance sustainability, efficiency, and reliability due to the increasing adoption of smart grid systems. To gather and share vast amounts of data in almost real time, these systems primarily rely on sophisticated sensors, communication, and computational capabilities, particularly in smart metering applications.