Deep Learning-based Detection of Stealth False Data Injection Attacks in Large-Scale Power Grids

基于深度学习的大规模电网隐形虚假数据注入攻击检测

• 批准号: 1808064
• 负责人: Katherine Davis
• 金额: $ 36万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1808064&HistoricalAwards=false
• 关键词: Deep; Learning; based; Detection; Stealth

The proposed research aims to strengthen national security by improving the resilience of power grid critical infrastructure with respect to data manipulation attacks. A comprehensive methodology referred to as DEFENDA - DEtection of FalsE and uNexpected Data Attacks - is proposed to quantify the integrity of data and to characterize the impact of false data on power systems. These attacks are referred to as unobservable or stealth false data injection (FDI) attacks, and they are crafted to bypass traditional bad data detection. DEFENDA's vision is to quickly detect sensor manipulation attacks and correct the false data. The project aims to contribute enhanced state-of-the-art cyber-physical security strategies for transmission system operation, where results will inform solution of similar problems including cyber-physical attack detection at generation, transmission, and distribution levels as well as in communication networks, banking systems, cloud computing and storage, and other critical infrastructures. DEFENDA will contribute attack detection strategies for real-world power grids via deep neural network (DNN) architectures known to offer superior representational power and improved detection performance. Specifically, DEFENDA aims to develop an efficient and robust FDI attack detection mechanism based on a deep long-short-term-memory (LSTM) recurrent neural network (RNN) that captures the time series nature of the status and measurement data and learns their respective normal and malicious patterns. To ensure detection efficiency, DEFENDA investigates optimal selection of the deep architecture and underlying hyper-parameters. Furthermore, DEFENDA ensures detection robustness through three measures. First, DEFENDA enables replacement of any missing status and measurement data via a deep LSTM auto-encoder (LSTM-AE) to enhance detection performance even in presence of jamming attacks. Second, using a deep variational LSTM auto-encoder (V-LSTM-AE) DEFENDA is capable to detect attacks that have not been characterized via an anomaly detector. Finally, DEFENDA carries out detection decision fusion based on centralized, semi-centralized, and decentralized detection architectures. DEFENDA will also create and make available synthetic cases with scenarios designed to promote research in cyber-physical analysis and attack detection. By demonstrating the importance of cyber security and data integrity though the scenarios developed, the project will prepare a generation to solve the problems facing society.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项拟议的研究旨在通过提高电网关键基础设施应对数据操纵攻击的弹性来加强国家安全。提出了一种被称为防御的综合方法，即检测虚假和意外的数据攻击，以量化数据的完整性并表征虚假数据对电力系统的影响。这些攻击被称为不可观察或隐形虚假数据注入(FDI)攻击，它们是为了绕过传统的坏数据检测而精心设计的。防御工事的愿景是快速检测传感器操纵攻击并纠正错误数据。该项目旨在为传输系统的运行提供增强的最先进的网络物理安全策略，其结果将为解决类似问题提供参考，包括在发电、传输和分配级别以及在通信网络、银行系统、云计算和存储以及其他关键基础设施中进行网络物理攻击检测。防御将通过深度神经网络(DNN)体系结构为现实世界的电网提供攻击检测策略，该体系结构可提供卓越的代表性能力和改进的检测性能。具体地说，防御旨在开发一种基于深度长期-短期记忆(LSTM)循环神经网络(RNN)的高效和健壮的FDI攻击检测机制，该网络捕获状态和测量数据的时间序列性质，并学习它们各自的正常和恶意模式。为了确保检测效率，防御方研究了深层架构和底层超参数的最佳选择。此外，防御通过三个措施确保检测的健壮性。首先，防御能够通过深度LSTM自动编码器(LSTM-AE)替换任何丢失的状态和测量数据，以提高检测性能，即使在存在干扰攻击的情况下也是如此。其次，使用深度变分LSTM自动编码器(V-LSTM-AE)防御能够检测到尚未通过异常检测器表征的攻击。最后，防御方基于集中式、半集中式和分散式检测体系结构进行检测决策融合。防御还将创建并提供合成案例，其场景旨在促进网络物理分析和攻击检测方面的研究。通过通过开发的场景展示网络安全和数据完整性的重要性，该项目将为解决社会面临的问题培养一代人。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Generating Connected, Simple, and Realistic Cyber Graphs for Smart Grids

为智能电网生成互联、简单且真实的网络图

• DOI: 10.1109/tpec54980.2022.9750688
• 发表时间: 2022
• 期刊: 2022 IEEE Texas Power and Energy Conference (TPEC
• 作者: Boyaci, Osman;Narimani, M. Rasoul;Davis, Katherine;Serpedin, Erchin
• 通讯作者: Serpedin, Erchin

Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids

• DOI: 10.48550/arxiv.2206.12527
• 发表时间: 2022-06
• 期刊: ArXiv
• 作者: Osman Boyaci;M. Narimani;K. Davis;E. Serpedin

Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks

• DOI: 10.1109/iceee55327.2022.9772523
• 发表时间: 2021-12
• 期刊: 2022 9th International Conference on Electrical and Electronics Engineering (ICEEE)
• 作者: Osman Boyaci;M. Narimani;K. Davis;E. Serpedin

A3D: Attention-based auto-encoder anomaly detector for false data injection attacks

• DOI: 10.1016/j.epsr.2020.106795
• 发表时间: 2020-12
• 期刊: Electric Power Systems Research
• 影响因子: 3.9
• 作者: Arnav Kundu;A. Sahu;E. Serpedin;K. Davis

Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids Using Graph Neural Networks

• DOI: 10.1109/tsg.2021.3117977
• 发表时间: 2021-04
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: Osman Boyaci;M. Narimani;K. Davis;Muhammad Ismail;T. Overbye;E. Serpedin