A Multi Agent Framework to Detect in Progress False Data Injection Attacks for Smart Grid

Sinha, Ayush; Thukkaraju, Ashrith Reddy; Vyas, O. P.

doi:10.1007/978-3-030-96040-7_10

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…proposed a RL-based detection method without the attack model, utilizing a Q-table to detect attacks by Sarsa algorithm. To address the complexity of storing Q-table, Sinha et al, 2022), applied DQN algorithm to detect FDIAs by neural networks. Moreover, (Alnowibet et al, 2021;Mohamed et al, 2021), studied FDIA detection on energy trading and energy management systems by intelligent priority selection-based RL method.…”

Section: Related Workmentioning

confidence: 99%

“…First, to defend against FDIAs before being attacked, researchers have studied the deployment of grid at the cyber-physical level, such as optimizing the distribution of key nodes and devices according to their coupling characteristics (Lei et al, 2020;Wu et al, 2021). Second, to detect FDIAs after attack, substantial efforts has been made, such as dynamic state estimation method (An et al, 2022), tracking the deviation of measurement (Alnowibet et al, 2021;Mohamed et al, 2021;Sinha et al, 2022) and some stochastic game methods (Wei et al, 2018;Oozeer and Haykin, 2019). In summary, above researches had shown that both cyber and physical methods are required in FDIA studies.…”

Section: Introductionmentioning

confidence: 99%

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False data injection attack in smart grid: Attack model and reinforcement learning-based detection method

Lin

An²,

Cui³

et al. 2023

Front. Energy Res.

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The smart grid, as a cyber-physical system, is vulnerable to attacks due to the diversified and open environment. The false data injection attack (FDIA) can threaten the grid security by constructing and injecting the falsified attack vector to bypass the system detection. Due to the diversity of attacks, it is impractical to detect FDIAs by fixed methods. This paper proposed a false data injection attack model and countering detection methods based on deep reinforcement learning (DRL). First, we studied an attack model under the assumption of unlimited attack resources and information of complete topology. Different types of FDIAs are also enumerated. Then, we formulated the attack detection problem as a Markov decision process (MDP). A deep reinforcement learning-based method is proposed to detect FDIAs with a combined dynamic-static detection mechanism. To address the sparse reward problem, experiences with discrepant rewards are stored in different replay buffers to achieve efficiency. Moreover, the state space is extended by considering the most recent states to improve the perception capability. Simulations were performed on IEEE 9,14,30, and 57-bus systems, proving the validation of attack model and efficiency of detection method. Results proved efficacy of the detection method in different scenarios.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

False data injection attack in smart grid: Attack model and reinforcement learning-based detection method

Lin

An²,

Cui³

et al. 2023

Front. Energy Res.

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“…• Dos/DDoS Attacks: In smart ecosystems, denial of service and distributed denial of service attacks are prevalent [97]. A hacker attempts to consume legitimate network resources or bandwidth during a DoS attack.…”

Section: ) the Network Layermentioning

confidence: 99%

“…Figure 5 illustrates seven types of attacks that are most encountered at this layer. The following discussion highlights key attacks in the network layer of the IoT: • Dos/DDoS Attacks: In smart ecosystems, denial of service (DoS) and distributed denial of service (DDoS) attacks are prevalent [95]. During a DoS attack, a hacker attempts to consume legitimate network resources or bandwidth.…”

Section: Figure 4: Cybersecurity Threats In Perception Layermentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a datadriven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.

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A Multi Agent Framework to Detect in Progress False Data Injection Attacks for Smart Grid

Cited by 2 publications

References 17 publications

False data injection attack in smart grid: Attack model and reinforcement learning-based detection method

False data injection attack in smart grid: Attack model and reinforcement learning-based detection method

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

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