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1. A Study of Linear Programming and Reinforcement Learning for One-Shot Game in Smart Grid Security

   https://doi.org/10.1109/IJCNN.2018.8489202  

   Paul, Shuva; Ni, Zhen (July 2018, 2018 International Joint Conference on Neural Networks (IJCNN))

   Smart grid attacks can be applied on a single component or multiple components. The corresponding defense strategies are totally different. In this paper, we investigate the solutions (e.g., linear programming and reinforcement learning) for one-shot game between the attacker and defender in smart power systems. We designed one-shot game with multi-line- switching attack and solved it using linear programming. We also designed the game with single-line-switching attack and solved it using reinforcement learning. The pay-off and utility/reward of the game is calculated based on the generation loss due to initiated attack by the attacker. Defender's defense action is considered while evaluating the pay-off from attacker's and defender's action. The linear programming based solution gives the probability of choosing best attack actions against different defense actions. The reinforcement learning based solution gives the optimal action to take under selected defense action. The proposed game is demonstrated on 6 bus system and IEEE 30 bus system and optimal solutions are analyzed. 

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2. Automated Adversary-in-the-Loop Cyber-Physical Defense Planning

   https://doi.org/10.1145/3596222  

   Banik, Sandeep; Ramachandran, Thiagarajan; Bhattacharya, Arnab; Bopardikar, Shaunak D. (July 2023, ACM Transactions on Cyber-Physical Systems)

   Security of cyber-physical systems (CPS) continues to pose new challenges due to the tight integration and operational complexity of the cyber and physical components. To address these challenges, this article presents a domain-aware, optimization-based approach to determine an effective defense strategy for CPS in an automated fashion—by emulating a strategic adversary in the loop that exploits system vulnerabilities, interconnection of the CPS, and the dynamics of the physical components. Our approach builds on an adversarial decision-making model based on a Markov Decision Process (MDP) that determines the optimal cyber (discrete) and physical (continuous) attack actions over a CPS attack graph. The defense planning problem is modeled as a non-zero-sum game between the adversary and defender. We use a model-free reinforcement learning method to solve the adversary’s problem as a function of the defense strategy. We then employ Bayesian optimization (BO) to find an approximatebest-responsefor the defender to harden the network against the resulting adversary policy. This process is iterated multiple times to improve the strategy for both players. We demonstrate the effectiveness of our approach on a ransomware-inspired graph with a smart building system as the physical process. Numerical studies show that our method converges to a Nash equilibrium for various defender-specific costs of network hardening. 

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3. A Novel Methodology for Cybersecurity Investment Optimization in Smart Grids using Attack-Defense Trees and Game Theory

   Hyder, Burhan; Govindarasu, Manimaran (February 2022, IEEE Innovative Smart Grid Technologies (ISGT))

   Securing cyber-physical systems (CPS) like the Smart Grid against cyber attacks is making it imperative for the system defenders to plan for investing in the cybersecurity resources of cyber-physical critical infrastructure. Given the constraint of limited resources that can be invested in the cyber layer of the cyber-physical smart grid, optimal allocation of these resources has become a priority for the defenders of the grid. This paper proposes a methodology for optimizing the allocation of resources for the cybersecurity infrastructure in a smart grid using attack-defense trees and game theory. The proposed methodology uses attack-defense trees (ADTs) for analyzing the cyber-attack paths (attacker strategies) within the grid and possible defense strategies to prevent those attacks. The attack-defense strategy space (ADSS) provides a comprehensive list of interactions between the attacker and the defender of the grid. The proposed methodology uses the ADSS from the ADT analysis for a game-theoretic formulation (GTF) of attacker-defender interaction. The GTF allows us to obtain strategies for the defender in order to optimize cybersecurity resource allocation in the smart grid. The implementation of the proposed methodology is validated using a synthetic smart grid model equipped with cyber and physical components depicting the feasibility of the methodology for real-world implementation. 

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4. Adversarial Deep Reinforcement Learning Based Adaptive Moving Target Defense

   Eghtesad, Taha; Vorobeychik, Yevgeniy; Laszka, Aron (January 2020, International Conference on Decision and Game Theory for Security)

   null (Ed.)

   Moving target defense (MTD) is a proactive defense approach that aims to thwart attacks by continuously changing the attack surface of a system (e.g., changing host or network configurations), thereby increasing the adversary’s uncertainty and attack cost. To maximize the impact of MTD, a defender must strategically choose when and what changes to make, taking into account both the characteristics of its system as well as the adversary’s observed activities. Finding an optimal strategy for MTD presents a significant challenge, especially when facing a resourceful and determined adversary who may respond to the defender’s actions. In this paper, we propose a multi-agent partially-observable Markov Decision Process model of MTD and formulate a two-player general-sum game between the adversary and the defender. To solve this game, we propose a multi-agent reinforcement learning framework based on the double oracle algorithm. Finally, we provide experimental results to demonstrate the effectiveness of our framework in finding optimal policies. 

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5. Clean and Compact: Efficient Data-Free Backdoor Defense with Model Compactness

   Phan, Huy; Xiao, Jinqi; Sui, Yang; Zhang, Tianfang; Tang, Zijie; Shi, Cong; Wang, Yan; Chen, Yingying; Yuan, Bo (September 2024, Springer, Cham)

   Deep neural networks (DNNs) have been widely deployed in real-world, mission-critical applications, necessitating effective approaches to protect deep learning models against malicious attacks. Motivated by the high stealthiness and potential harm of backdoor attacks, a series of backdoor defense methods for DNNs have been proposed. However, most existing approaches require access to clean training data, hindering their practical use. Additionally, state-of-the-art (SOTA) solutions cannot simultaneously enhance model robustness and compactness in a data-free manner, which is crucial in resource-constrained applications. To address these challenges, in this paper, we propose Clean & Compact (C&C), an efficient data-free backdoor defense mechanism that can bring both purification and compactness to the original infected DNNs. Built upon the intriguing rank-level sensitivity to trigger patterns, C&C co-explores and achieves high model cleanliness and efficiency without the need for training data, making this solution very attractive in many real-world, resource-limited scenarios. Extensive evaluations across different settings consistently demonstrate that our proposed approach outperforms SOTA backdoor defense methods. 

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