Guarding the Grid: Enhancing Resilience in Automated Residential Demand Response Against False Data Injection Attacks

• URL: http://arxiv.org/abs/2312.08646v1
• Date: Thu, 14 Dec 2023 04:02:52 GMT
• Title: Guarding the Grid: Enhancing Resilience in Automated Residential Demand Response Against False Data Injection Attacks
• Authors: Thusitha Dayaratne, Carsten Rudolph, Ariel Liebman, Mahsa Salehi,
• Abstract summary: Utility companies are increasingly leveraging residential demand flexibility and the proliferation of smart/IoT devices to enhance the effectiveness of demand response programs. The adoption of distributed architectures in these systems exposes them to the risk of false data injection attacks (FDIAs) We present a comprehensive framework that combines DR optimisation, anomaly detection, and strategies for mitigating the impacts of attacks to create a resilient and automated device scheduling system.
• Score: 2.981139602986498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Utility companies are increasingly leveraging residential demand flexibility and the proliferation of smart/IoT devices to enhance the effectiveness of residential demand response (DR) programs through automated device scheduling. However, the adoption of distributed architectures in these systems exposes them to the risk of false data injection attacks (FDIAs), where adversaries can manipulate decision-making processes by injecting false data. Given the limited control utility companies have over these distributed systems and data, the need for reliable implementations to enhance the resilience of residential DR schemes against FDIAs is paramount. In this work, we present a comprehensive framework that combines DR optimisation, anomaly detection, and strategies for mitigating the impacts of attacks to create a resilient and automated device scheduling system. To validate the robustness of our framework against FDIAs, we performed an evaluation using real-world data sets, highlighting its effectiveness in securing residential DR systems.

Related papers

• Robust Federated Learning Against Poisoning Attacks: A GAN-Based Defense Framework [0.6554326244334868]
  Federated Learning (FL) enables collaborative model training across decentralized devices without sharing raw data. We propose a privacy-preserving defense framework that leverages a Conditional Generative Adversarial Network (cGAN) to generate synthetic data at the server for authenticating client updates.
  arXiv  Detail & Related papers  (2025-03-26T18:00:56Z)
• Optimal Security Response to Network Intrusions in IT Systems [0.0]
  This thesis tackles the challenges by developing a practical methodology for optimal security response in IT infrastructures. First, it includes an emulation system that replicates key components of the target infrastructure. Second, it includes a simulation system where game-theoretic response strategies are optimized through approximation model.
  arXiv  Detail & Related papers  (2025-02-04T18:10:10Z)
• Preventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters [99.90150979732641]
  We propose an innovative scheme based on adversarial attack in this paper. The scheme effectively prevents NILM models from violating appliance-level privacy, while also ensuring accurate billing calculation for users. Our solutions exhibit transferability, making the generated perturbation signal from one target model applicable to other diverse NILM models.
  arXiv  Detail & Related papers  (2024-12-22T07:06:46Z)
• Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks [19.697853431302768]
  Digital twins (DTs) embody real-time monitoring, predictive, and enhanced decision-making capabilities. This study investigates the security challenges in distributed network DT systems, which potentially undermine the reliability of subsequent network applications.
  arXiv  Detail & Related papers  (2024-07-02T03:32:09Z)
• ADVREPAIR:Provable Repair of Adversarial Attack [15.580097790702508]
  Deep neural networks (DNNs) are increasingly deployed in safety-critical domains, but their vulnerability to adversarial attacks poses serious safety risks. Existing neuron-level methods using limited data lack efficacy in fixing adversaries due to the complexity of adversarial attack mechanisms. We propose ADVREPAIR, a novel approach for provable repair of adversarial attacks using limited data.
  arXiv  Detail & Related papers  (2024-04-02T05:16:59Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Enhancing IoT Security Against DDoS Attacks through Federated Learning [0.0]
  Internet of Things (IoT) has ushered in transformative connectivity between physical devices and the digital realm. Traditional DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT ecosystems. This paper introduces an innovative strategy to bolster the security of IoT networks against DDoS attacks by harnessing the power of Federated Learning.
  arXiv  Detail & Related papers  (2024-03-16T16:45:28Z)
• Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation [2.28438857884398]
  This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a consensus-based verification process integrated with an adaptive thresholding mechanism. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience.
  arXiv  Detail & Related papers  (2024-03-05T20:54:56Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Deep Generative Attacks and Countermeasures for Data-Driven Offline Signature Verification [2.0368479127360093]
  This study investigates the vulnerabilities of data-driven offline signature verification (DASV) systems to generative attacks. We explore the efficacy of Variversaational Autoencoders (VAEs) and Conditional Generative Adrial Networks (CGANs) in creating deceptive signatures that challenge DASV systems.
  arXiv  Detail & Related papers  (2023-12-02T00:58:34Z)
• TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework [58.474610046294856]
  Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. This paper introduces an integrated framework that leverages the capabilities of the Transformer model-based neural networks and deep reinforcement learning (DRL) algorithms to optimize system maintenance actions.
  arXiv  Detail & Related papers  (2023-09-29T02:27:54Z)
• Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation [127.6168183074427]
  We propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID. This is challenging because it requires the model to continuously adapt to unlabeled data of the target environments. We design an effective scheme for this task, dubbed CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the adaptation.
  arXiv  Detail & Related papers  (2021-12-13T13:19:45Z)
• Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training [55.824641135682725]
  Domain adaptation experiments using WSJ as a source domain and TED-LIUM 3 as well as SWITCHBOARD show that up to 80% of the performance of a system trained on ground-truth data can be recovered.
  arXiv  Detail & Related papers  (2020-11-26T18:51:26Z)
• Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach [68.40429597811071]
  The risk of unplanned equipment downtime can be minimized through Predictive Maintenance of revenue generating assets. A model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context. Unlike traditional black-box regression models, the proposed algorithm self-learns an optimal maintenance policy and provides actionable recommendation for each equipment.
  arXiv  Detail & Related papers  (2020-07-07T10:00:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.