Attention-aware deep reinforcement learning for detecting false data injection attacks in smart grids

Huang, Rong; Li, Yuancheng; Wang, Xiao

doi:10.1016/j.ijepes.2022.108815

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Threat analysis helps identify potential vulnerabilities and assess their impact on business operations. It is a quantitative and qualitative assessment of critical organizational resources to determine the business impact on Information Technology and Information Systems IT/IS [39], [40].…”

Section: B Scadamentioning

confidence: 99%

Leveraging Deep Reinforcement Learning Technique for Intrusion Detection in SCADA Infrastructure

Mesadieu,

Torre,

Chennameneni

2024

IEEE Access

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The prevalence of cyber-attacks perpetrated over the last two decades, including coordinated attempts to breach targeted organizations, has drastically and systematically exposed some of the more critical vulnerabilities existing in our cyber ecosystem. Particularly in Supervisory Control and Data Acquisition (SCADA) systems with targeted attacks aiming to bypass signature-based protocols, attempting to gain control over operational processes. In the past, researchers utilized deep learning and reinforcement learning algorithms to mitigate threats against industrial control systems (ICS). However, as technology evolves, these techniques become ineffective in monitoring and enhancing the cybersecurity defenses of those system against unwanted attacks. To address these concerns, we propose a deep reinforcement learning (DRL) framework for anomaly detection in the SCADA network. Our model utilizes a "Q-network", which allows it to achieve state-of-the-art performance in pattern recognition from complex tasks. We validated our solution on two publicly available datasets. The WUSTL-IIoT-2018 and the WUSTL-IIoT-2021, each comprised of twenty-five networking features representing benign and attack traffic. The results showed that our model successfully achieved an accuracy of 99.36% in attack detection, highlighting DRL's potential to enhance the security of critical infrastructure and laying the foundation for future research in this domain.

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Section: B Scadamentioning

confidence: 99%

Leveraging Deep Reinforcement Learning Technique for Intrusion Detection in SCADA Infrastructure

Mesadieu,

Torre,

Chennameneni

2024

IEEE Access

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“…Using deep learning as auxiliaries [52,66,72,74] Simply developing classifiers for detection [53,54,69,76,77,81] Locating false data injection attacks [55,56,60,63,68,75] Resorting to deep reinforcement learning for detection [64,65,79,95] Detecting attacks with specific targets [57,70,78,80] Addressing the problem of attack samples insufficiency [58,59,67,83] Considering disturbances from renewable energy integration [60,61] Handling the privacy problem in constructing detectors [62,71,73] [51] designed novel FDIA strategies by introducing adversarial samples (also called perturbation vectors) into FDIAs, thereby deceiving BDDs and DL-based detectors.…”

Section: Classification Literaturementioning

confidence: 99%

“…[65] proposed a DRL-based detection approach that combines the LSTM network to extract the system state features from previous time steps and consequently determine whether the system is currently being attacked. Similar to [64,65] added an AM to the DRL-based detection algorithm, which calculated the attention distribution of all input information, and the SE results were dynamically weighted according to the attention distribution. In this manner, it can extract more representative and distinguishable state features as observations, which can better facilitate decisionmaking in DRL for FDIA detection.…”

Section: Classification Literaturementioning

confidence: 99%

Deep learning for cybersecurity in smart grids: Review and perspectives

Ruan

Liang

Zhao

et al. 2023

Energy Conversion and Econom

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Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered significant attention in recent years. The application of artificial intelligence (AI), particularly deep learning (DL), holds great promise for enhancing the cybersecurity of SG. Nevertheless, previous surveys and review articles have failed to comprehensively investigate the intersection between DL and SG cybersecurity. To address this gap, this study presents a survey of the latest advancements in DL technology and their relevance to SG cybersecurity. First, the functional mechanisms and scope of application of common DL techniques are explored. Subsequently, SG cyberthreats are categorised into distinct types of cyberattacks that have not been systematically examined in previous surveys. Based on this, a thorough review of the application of DL techniques in addressing each cyberthreat along with recommendations and a generalised framework for enhancing cyberattack detection using DL is offered. Finally, insights are provided into the emerging challenges presented by DL applications in SG cybersecurity that are yet to be widely acknowledged, and potential research avenues are proposed to address or alleviate these challenges.

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“…They pay attention to the detection and defense methods of FDIA and put forward a variety of different solutions. For example, Huang et al proposed a deep reinforcement learning FDIA detection method, which focuses on state attention to solve the problem of state feature extraction in existing reinforcement learning detection methods [9]. Mahi et al proposed a novel prediction-aided anomaly detection, using the sequence-to-sequence architecture of the automatic encoder based on CNN-LSTM to combat the FDIA [10].…”

Section: Introductionmentioning

confidence: 99%

Laplace-Domain Hybrid Distribution Model Based FDIA Attack Sample Generation in Smart Grids

Wu,

Zu,

Guo

et al. 2023

Symmetry

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False data injection attack (FDIA) is a deliberate modification of measurement data collected by the power grid using vulnerabilities in power grid state estimation, resulting in erroneous judgments made by the power grid control center. As a symmetrical defense scheme, FDIA detection usually uses machine learning methods to detect attack samples. However, existing detection models for FDIA typically require large-scale training samples, which are difficult to obtain in practical scenarios, making it difficult for detection models to achieve effective detection performance. In light of this, this paper proposes a novel FDIA sample generation method to construct large-scale attack samples by introducing a hybrid Laplacian model capable of accurately fitting the distribution of data changes. First, we analyze the large-scale power system sensing measurement data and establish the data distribution model of symmetric Laplace distribution. Furthermore, a hybrid Laplace-domain symmetric distribution model with multi-dimensional component parameters is constructed, which can induce a deliberate deviation in the state estimation from its safe value by injecting into the power system measurement. Due to the influence of the multivariate parameters of the hybrid Laplace-domain distribution model, the sample deviation generated by this model can not only obtain an efficient attack effect, but also effectively avoid the recognition of the FDIA detection model. Extensive experiments are carried out over IEEE 14-bus and IEEE 118-bus test systems. The corresponding results unequivocally demonstrate that our proposed attack method can quickly construct large-scale FDIA attack samples and exhibit significantly higher resistance to detection by state-of-the-art detection models, while also offering superior concealment capabilities compared to traditional FDIA approaches.

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Attention-aware deep reinforcement learning for detecting false data injection attacks in smart grids

Cited by 7 publications

References 29 publications

Leveraging Deep Reinforcement Learning Technique for Intrusion Detection in SCADA Infrastructure

Leveraging Deep Reinforcement Learning Technique for Intrusion Detection in SCADA Infrastructure

Deep learning for cybersecurity in smart grids: Review and perspectives

Laplace-Domain Hybrid Distribution Model Based FDIA Attack Sample Generation in Smart Grids

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