Load-Altering Attacks Against Power Grids: A Case Study Using the GB-36 Bus System Open Dataset

• URL: http://arxiv.org/abs/2508.08945v1
• Date: Tue, 12 Aug 2025 13:57:16 GMT
• Title: Load-Altering Attacks Against Power Grids: A Case Study Using the GB-36 Bus System Open Dataset
• Authors: Syed Irtiza Maksud, Subhash Lakshminarayana,
• Abstract summary: Load Altering Attacks (LAAs) can trigger rapid frequency fluctuations and potentially destabilize the power grid.<n>This paper aims to bridge the gap between academic research and practical application by using open-source datasets released by grid operators.<n>It investigates various LAA scenarios on a real-world transmission network, namely the Great Britain (GB)-36 Zone model released by the UK's National Electricity System Operator (NESO)
• Score: 0.2455468619225742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing digitalization and the rapid adoption of high-powered Internet-of-Things (IoT)-enabled devices (e.g., EV charging stations) have increased the vulnerability of power grids to cyber threats. In particular, the so-called Load Altering Attacks (LAAs) can trigger rapid frequency fluctuations and potentially destabilize the power grid. This paper aims to bridge the gap between academic research and practical application by using open-source datasets released by grid operators. It investigates various LAA scenarios on a real-world transmission network, namely the Great Britain (GB)-36 Zone model released by the UK's National Electricity System Operator (NESO). It evaluates the threshold of LAA severity that the grid can tolerate before triggering cascading effects. Additionally, it explores how Battery Energy Storage Systems (BESS) based fast frequency response services can mitigate or prevent such impacts. Simulations are conducted using DIgSILENT PowerFactory to ensure realistic system representation. The analysis provides several useful insights to grid operators on the LAA impact, such as the influence of the relative locations of BESS and LAA, as well as how delays in attack execution can influence the overall system response.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Cyber-Physical Co-Simulation of Load Frequency Control under Load-Altering Attacks [1.1662899857778717]
  Recently emerging Load Altering Attacks (LAAs) utilize a botnet of high-wattage devices to introduce load fluctuation.<n>This paper presents an open-source co-simulation environment that models the power grid with the corresponding communication network, implementing grid protective mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T13:52:09Z)
• Assessment of Quantitative Cyber-Physical Reliability of SCADA Systems in Autonomous Vehicle to Grid (V2G) Capable Smart Grids [0.0]
  The integration of electric vehicles (EVs) into power grids via Vehicle-to-Grid (V2G) system technology is increasing day by day.<n>V2G can increase grid reliability by providing distributed energy storage and ancillary services.<n>It has a scope that encompasses the cyber-physical attack surface of the national power grid, introducing new vulnerabilities in monitoring and supervisory control and data acquisition (SCADA) systems.<n>This paper investigates the maliciousness caused by Autonomous Vehicle to Grid (AV2G) communication infrastructures and assesses their impacts on SCADA system reliability.
  arXiv  Detail & Related papers  (2025-07-24T06:57:10Z)
• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• Exploring the Effects of Load Altering Attacks on Load Frequency Control through Python and RTDS [1.1662899857778717]
  Load altering attacks (LAAs), which use botnets of high-wattage devices to manipulate load profiles, are a notable threat to grid stability.<n>This study bridges the gap by analyzing LAA effects on load frequency control (LFC) through simulations of static and dynamic scenarios.<n>The results highlight LAA impacts on frequency stability and present an eigenvalue-based stability assessment for dynamic LAAs.
  arXiv  Detail & Related papers  (2025-04-11T20:07:47Z)
• Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices [3.858859576352153]
  Mobile edge computing in the form of unmanned aerial vehicles (UAVs) has been proposed to provide offloading from these devices to conserve their battery.<n>This paper considers the use of UAVs with further constraints on power and connectivity to prolong the life of the network.<n>Reinforcement learning is used to investigate numerous scenarios of various levels of power and communication failure.
  arXiv  Detail & Related papers  (2025-01-25T19:03:05Z)
• 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)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Physics-Constrained Backdoor Attacks on Power System Fault Localization [1.1683938179815823]
  This work proposes a novel physics-constrained backdoor poisoning attack. It embeds the undetectable attack signal into the learned model and only performs the attack when it encounters the corresponding signal. The proposed attack pipeline can be easily generalized to other power system tasks.
  arXiv  Detail & Related papers  (2022-11-07T12:57:26Z)
• Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network [62.77129284830945]
  We show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. We benchmark the performance of these attacks and show that with a small perturbation in the input of the neural network (NN), the white-box attacks can result in infeasible solutions up to 86%.
  arXiv  Detail & Related papers  (2021-01-28T16:18:19Z)

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.