RL-BAGS: A Tool for Smart Grid Risk Assessment

Wadhawan, Yatin; Neuman, Clifford

doi:10.1109/icsgce.2018.8556775

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…This approach ensures continuous adaptation to changes while preserving data privacy. Article [177] details RL-BAGS tool: Reinforcement Learning-Bayesian Attack Graph for Smart Grid System, which uses RL algorithms to re ne and update security strategies based on ongoing risk assessments. This tool enhances the resilience of smart grids by ensuring that security measures are progressively aligned with the latest cybersecurity developments.…”

Section: Continuous Improvement and Optimizationmentioning

confidence: 99%

Artificial intelligence for system security assurance: A systematic literature review

Wen,

Shukla,

Katt

2024

Preprint

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System Security Assurance (SSA) has emerged as a critical methodology for organizations to verify the trustworthiness of their systems by evaluating security measures against industry standards, legal requirements, and best practices to identify any weakness and demonstrate compliance. In recent years, the role of Artificial Intelligence (AI) in enhancing cybersecurity has received increased attention, with an increasing number of literature reviews highlighting its diverse applications. However, there remains a significant gap in comprehensive reviews that specifically address the integration of AI within SSA frameworks. This systematic literature review seeks to fill this research gap by assessing the current state of AI in SSA, identifying key areas where AI contributes to improve SSA processes, highlighting the limitations of current methodologies, and providing the guidance for future advancements in the field of AI-driven SSA.

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Section: Continuous Improvement and Optimizationmentioning

confidence: 99%

Artificial intelligence for system security assurance: A systematic literature review

Wen,

Shukla,

Katt

2024

Preprint

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“…addressed all aspects of the risk management process and methodologies that must be modified to achieve project objectives [138]. Risks to the are among the risks to the smart grid most openly discussed and documented [140].…”

Section: ) Risk Analysis and Management Manual (Ramm)mentioning

confidence: 99%

A Survey of Hydrogen Energy and I-Energy Applications: Household Intelligent Electrical Power Systems

Sami

2020

IEEE Access

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The authors would like to thank the DSR technical and financial support. ABSTRACT I-energy and its applications used in the smart grid are chosen as attractive research issues due to their widespread applications in the smart home. In this vein, the optimization of energy demand in smart home appliances, in a smart grid, is considered the main challenge faced by power companies during peak periods regarding its considerable effect on the global power system stabilities. Therefore, this paper aims to show explicitly the performance of smart grid on electricity and hydrogen applications with reused communication methods. To facilitate the understanding of the communication-reused methods in the smart grid, several components are identified and presented in terms of improvements, benefits and lack of features. In the smart grid, most innovation communications are being used as wireline and wireless bundle, where the main features are aggregated. Indeed, reducing consumers' electricity bills in smart home electrical systems relies on increasing the relationship between the highest energy demand and annual energy use. The most appropriate research has been reviewed on optimization approaches discussing the smart home electric system. The examined methods are categorized into trustworthy and metaheuristic algorithms. INDEX TERMS Smart home, I-energy concept, hydrogen-renewable energy system, smart meter, smart grid, risk management, demand-side management, AMI.

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“…In this section, we extend the BAGS functionality by implementing Reinforcement Learning for Bayesian Attack Graph for Smart Grid System (RL-BAGS) [14] to provide the ability to compute optimal policies on regular intervals of whether to scan or patch a cyber function of SG. RL-BAGS implements two RL algorithms: Q-learning and State-Action-Reward-State-Action (SARSA) learning, on the generated FBN (BAGS).…”

Section: Resource Allocation In the Smart Gridmentioning

confidence: 99%

“…If engineers give importance to functions that are present at the start of the graph, the agent will learn a policy to scan and patch those functions first, even if functions present in the end are vulnerable. For more details, refer [14].…”

Section: Simulation Analysismentioning

confidence: 99%

“…(1) the function-based methodology to evaluate SGR [9][10][11][12], (2) Bayesian Attack Graph for Smart Grid (BAGS) to compute the likelihood of the compromise of cyber components of SG [13], (3) the risk analysis methodology, which combines the results of the function-based methodology and BAGS to compute risk for each cyber component and (4) efficient resource allocation using Reinforcement learning on the SG Bayesian graph (BAGS) [14] to compute optimal policies about whether to perform vulnerability assessment or patch a cyber component of SG whose vulnerability has already been discovered. The results and analysis of these approaches help power engineers develop more resilient power systems and improve situational awareness and the response of the system to ongoing CPA.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks

2018

Self Cite

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In this paper, we present a comprehensive study of smart grid security against cyber-physical attacks on its distinct functional components. We discuss: (1) a function-based methodology to evaluate smart grid resilience against cyber-physical attacks; (2) a Bayesian Attack Graph for Smart Grid (BAGS) tool to compute the likelihood of the compromise of cyber components of the smart grid system; (3) risk analysis methodology, which combines the results of the function-based methodology and BAGS to quantify risk for each cyber component of the smart grid; and (4) efficient resource allocation in the smart grid cyber domain using reinforcement learning (extension of BAGS tool) to compute optimal policies about whether to perform vulnerability assessment or patch a cyber system of the smart grid whose vulnerability has already been discovered. The results and analysis of these approaches help power engineers to identify failures in advance from one system component to another, develop robust and more resilient power systems and improve situational awareness and the response of the system to cyber-physical attacks. This work sheds light on the interdependency between the cyber domain and power grid and demonstrates that the security of both worlds requires the utmost attention. We hope this work assists power engineers to protect the grid against future cyber-physical attacks.

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RL-BAGS: A Tool for Smart Grid Risk Assessment

Cited by 11 publications

References 7 publications

Artificial intelligence for system security assurance: A systematic literature review

Artificial intelligence for system security assurance: A systematic literature review

A Survey of Hydrogen Energy and I-Energy Applications: Household Intelligent Electrical Power Systems

A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks

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