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Targeted Attack Synthesis for Smart Grid Vulnerability Analysis

Published: 21 November 2023 Publication History

Abstract

Modern smart grids utilize advanced sensors and digital communication to manage the flow of electricity from generation source to consumption points. They also employ anomaly detection units and phasor measurement units (PMUs) for security and monitoring of grid behavior. However, as smart grids are distributed, vulnerability analysis is necessary to identify and mitigate potential security threats targeting the sensors and communication links. We propose a novel algorithm that uses measurement parameters, such as power flow or load flow, to identify the smart grid's most vulnerable operating intervals. Our methodology incorporates a Monte Carlo simulation approach to identify these intervals and deploys a deep reinforcement learning agent to generate attack vectors during the identified intervals that can compromise the grid's safety and stability in the minimum possible time, while remaining undetected by local anomaly detection units and PMUs. Our approach provides a structured methodology for effective smart grid vulnerability analysis, enabling system operators to analyze the impact of attack parameters on grid safety and stability and facilitating suitable design changes in grid topology and operational parameters.

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  • (2024) SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3670293(645-659)Online publication date: 9-Dec-2024
  • (2024)Adaptive Protection of Power Grids against Stealthy Load Alterations2024 ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS)10.1109/ICCPS61052.2024.00038(285-286)Online publication date: 13-May-2024
  • (2024)An Adaptive Interpretable Safe-RL Approach for Addressing Smart Grid Supply-Side UncertaintiesExplainable and Transparent AI and Multi-Agent Systems10.1007/978-3-031-70074-3_7(116-136)Online publication date: 6-May-2024

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  1. Targeted Attack Synthesis for Smart Grid Vulnerability Analysis

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    cover image ACM Conferences
    CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
    November 2023
    3722 pages
    ISBN:9798400700507
    DOI:10.1145/3576915
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 21 November 2023

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    Author Tags

    1. counter-example traces
    2. deep reinforcement learning
    3. gps spoofing
    4. smart grid
    5. vulnerability analysis

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    View all
    • (2024) SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3670293(645-659)Online publication date: 9-Dec-2024
    • (2024)Adaptive Protection of Power Grids against Stealthy Load Alterations2024 ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS)10.1109/ICCPS61052.2024.00038(285-286)Online publication date: 13-May-2024
    • (2024)An Adaptive Interpretable Safe-RL Approach for Addressing Smart Grid Supply-Side UncertaintiesExplainable and Transparent AI and Multi-Agent Systems10.1007/978-3-031-70074-3_7(116-136)Online publication date: 6-May-2024

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