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research-article

LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection

Authors:

Qian LiAuthors Info & Claims

IEEE Transactions on Dependable and Secure Computing, Volume 21, Issue 5

Pages 4418 - 4432

https://doi.org/10.1109/TDSC.2024.3353302

Published: 12 January 2024 Publication History

Abstract

Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label FDIA detection, the adversarial attack and defense against multi-label FDIA locational detection are still not involved. To bridge this gap, this paper first explores the multi-label adversarial example attacks against multi-label FDIA locational detectors and proposes a general multi-label adversarial attack framework, namely muLti-labEl adverSarial falSe data injectiON attack (LESSON). The proposed LESSON attack framework includes three key designs, namely Perturbing State Variables, Tailored Loss Function Design, and Change of Variables, which can help find suitable multi-label adversarial perturbations within the physical constraints to circumvent both Bad Data Detection (BDD) and Neural Attack Location (NAL). Four typical LESSON attacks based on the proposed framework and two dimensions of attack objectives are examined, and the experimental results demonstrate the effectiveness of the proposed attack framework, posing serious and pressing security concerns in smart grids.

References

[1]

M. A. Rahman and A. Datta, “Impact of stealthy attacks on optimal power flow: A simulink-driven formal analysis,” IEEE Trans. Dependable Secure Comput., vol. 17, no. 3, pp. 451–464, May/Jun. 2020.

[2]

H. Zhang, B. Liu, X. Liu, A. Pahwa, and H. Wu, “Voltage stability constrained moving target defense against net load redistribution attacks,” IEEE Trans. Smart Grid, vol. 13, no. 5, pp. 3748–3759, Sep. 2022.

[3]

M. Liu, C. Zhao, J. Xia, R. Deng, P. Cheng, and J. Chen, “PDDL: Proactive distributed detection and localization against stealthy deception attacks in DC microgrids,” IEEE Trans. Smart Grid, vol. 14, no. 1, pp. 714–731, Jan. 2023.

[4]

G. Cheng, Y. Lin, J. Zhao, and J. Yan, “A highly discriminative detector against false data injection attacks in AC state estimation,” IEEE Trans. Smart Grid, vol. 13, no. 3, pp. 2318–2330, May 2022.

[5]

M. Farajzadeh-Zanjani, E. Hallaji, R. Razavi-Far, and M. Saif, “Generative-adversarial class-imbalance learning for classifying cyber-attacks and faults - a cyber-physical power system,” IEEE Trans. Dependable Secure Comput., vol. 19, no. 6, pp. 4068–4081, Nov./Dec. 2022.

[6]

J. Tian, B. Wang, T. Li, F. Shang, K. Cao, and R. Guo, “TOTAL: Optimal protection strategy against perfect and imperfect false data injection attacks on power grid cyber-physical systems,” IEEE Internet Things J., vol. 8, no. 2, pp. 1001–1015, Jan. 2021.

[7]

A. S. Musleh, G. Chen, and Z. Y. Dong, “A survey on the detection algorithms for false data injection attacks in smart grids,” IEEE Trans. Smart Grid, vol. 11, no. 3, pp. 2218–2234, May 2020.

[8]

S. Wang, S. Bi, and Y. J. A. Zhang, “Locational detection of the false data injection attack in a smart grid: A multilabel classification approach,” IEEE Internet Things J., vol. 7, no. 9, pp. 8218–8227, Sep. 2020.

[9]

P. Srinivasan V., K. Balasubadra, K. Saravanan, V.S. Arjun, and S. Malarkodi, “Multi label deep learning classification approach for false data injection attacks in smart grid,” KSII Trans. Internet Inf. Syst., vol. 15, no. 6, pp. 2168–2187, Jun. 2021.

[10]

D. Mukherjee, S. Chakraborty, and S. Ghosh, “Deep learning-based multilabel classification for locational detection of false data injection attack in smart grids,” Elect. Eng., vol. 104, no. 1, pp. 259–282, Feb. 2022.

[11]

D. Mukherjee, “A novel strategy for locational detection of false data injection attack,” Sustain. Energy, Grids Netw., vol. 31, 2022, Art. no.

[12]

H. I. Hegazy, A. S. Tag Eldien, M. M. Tantawy, M. M. Fouda, and H. A. TagElDien, “Real-time locational detection of stealthy false data injection attack in smart grid: Using multivariate-based multi-label classification approach,” Energies, vol. 15, no. 14, 2022, Art. no.

[13]

Z. Qin and Y. Lai, “Detection and localization of coordinated state-and-topology false data injection attack by multi-modal learning,” J. Elect. Eng. Technol., vol. 17, no. 5, pp. 2649–2662, Sep. 2022.

[14]

O. Boyaci, M. R. Narimani, K. R. Davis, M. Ismail, T. J. Overbye, and E. Serpedin, “Joint detection and localization of stealth false data injection attacks in smart grids using graph neural networks,” IEEE Trans. Smart Grid, vol. 13, no. 1, pp. 807–819, Jan. 2022.

[15]

Y. Han, H. Feng, K. Li, and Q. Zhao, “False data injection attacks detection with modified temporal multi-graph convolutional network in smart grids,” Comput. Secur., vol. 124, 2023, Art. no.

[16]

Y. Chen, Y. Tan, and D. Deka, “Is machine learning in power systems vulnerable?,” in Proc. IEEE Int. Conf. Commun. Control Comput. Technol. Smart Grids, 2018, pp. 1–6.

[17]

C. Ren, X. Du, Y. Xu, Q. Song, Y. Liu, and R. Tan, “Vulnerability analysis, robustness verification, and mitigation strategy for machine learning-based power system stability assessment model under adversarial examples,” IEEE Trans. Smart Grid, vol. 13, no. 2, pp. 1622–1632, Mar. 2022.

[18]

Y. Cheng, K. Yamashita, and N. Yu, “Adversarial attacks on deep neural network-based power system event classification models,” in Proc. IEEE PES Innov. Smart Grid Technol. Asia, 2022, pp. 66–70.

[19]

J. Tian, B. Wang, Z. Wang, K. Cao, J. Li, and M. Ozay, “Joint adversarial example and false data injection attacks for state estimation in power systems,” IEEE Trans. Cybern., vol. 52, no. 12, pp. 13699–13713, Dec. 2022.

[20]

J. Tian, B. Wang, J. Li, Z. Wang, B. Ma, and M. Ozay, “Exploring targeted and stealthy false data injection attacks via adversarial machine learning,” IEEE Internet Things J., vol. 9, no. 15, pp. 14116–14125, Aug. 2022.

[21]

J. Li, Y. Yang, J. S. Sun, K. Tomsovic, and H. Qi, “ConAML: Constrained adversarial machine learning for cyber-physical systems,” in Proc. ACM Asia Conf. Comput. Commun. Secur., 2021, pp. 52–66.

[22]

X. Niu, J. Li, J. Sun, and K. Tomsovic, “Dynamic detection of false data injection attack in smart grid using deep learning,” in Proc. IEEE Power Energy Soc. Innov. Smart Grid Technol. Conf., 2019, pp. 1–6.

[23]

Y. He, G. J. Mendis, and J. Wei, “Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism,” IEEE Trans. Smart Grid, vol. 8, no. 5, pp. 2505–2516, Sep. 2017.

[24]

X. Yin, Y. Zhu, and J. Hu, “A subgrid-oriented privacy-preserving microservice framework based on deep neural network for false data injection attack detection in smart grids,” IEEE Trans. Ind. Inform., vol. 18, no. 3, pp. 1957–1967, Mar. 2022.

[25]

X. Yin, Y. Zhu, Y. Xie, and J. Hu, “PowerFDNet: Deep learning-based stealthy false data injection attack detection for AC-model transmission systems,” IEEE Open J. Comput. Soc., vol. 3, pp. 149–161, 2022.

[26]

B. Xu, F. Guo, C. Wen, R. Deng, and W.-A. Zhang, “Detecting false data injection attacks in smart grids with modeling errors: A deep transfer learning based approach,” 2021,.

[27]

Y. Li, X. Wei, Y. Li, Z. Dong, and M. Shahidehpour, “Detection of false data injection attacks in smart grid: A secure federated deep learning approach,” IEEE Trans. Smart Grid, vol. 13, no. 6, pp. 4862–4872, Nov. 2022.

[28]

J. Tian, B. Wang, R. Guo, Z. Wang, K. Cao, and X. Wang, “Adversarial attacks and defenses for deep-learning-based unmanned aerial vehicles,” IEEE Internet Things J., vol. 9, no. 22, pp. 22399–22409, Nov. 2022.

[29]

J. Lian, X. Wang, Y. Su, M. Ma, and S. Mei, “CBA: Contextual background attack against optical aerial detection in the physical world,” IEEE Trans. Geosci. Remote Sens., vol. 61, 2023, Art. no.

[30]

J. Tian, B. Wang, J. Li, and Z. Wang, “Adversarial attacks and defense for CNN based power quality recognition in smart grid,” IEEE Trans. Netw. Sci. Eng., vol. 9, no. 2, pp. 807–819, Mar./Apr. 2022.

[31]

J. Lian, S. Mei, S. Zhang, and M. Ma, “Benchmarking adversarial patch against aerial detection,” IEEE Trans. Geosci. Remote Sens., vol. 60, 2022, Art. no.

[32]

K. Tang et al., “Rethinking perturbation directions for imperceptible adversarial attacks on point clouds,” IEEE Internet Things J., vol. 10, no. 6, pp. 5158–5169, Mar. 2023.

[33]

P. Zhu, J. Hong, X. Li, K. Tang, and Z. Wang, “SGMA: A novel adversarial attack approach with improved transferability,” Complex Intell. Syst., vol. 9, pp. 6051–6063, 2023.

[34]

J. Ruan et al., “On vulnerability of renewable energy forecasting: Adversarial learning attacks,” IEEE Trans. Ind. Inform., pp. 1–14, 2023.

[35]

M. M. Badr, M. M. E. A. Mahmoud, M. Abdulaal, A. J. Aljohani, F. Alsolami, and A. Balamsh, “A novel evasion attack against global electricity theft detectors and a countermeasure,” IEEE Internet Things J., vol. 10, no. 12, pp. 11038–11053, Jun. 2023.

[36]

Q. Song et al., “On credibility of adversarial examples against learning-based grid voltage stability assessment,” IEEE Trans. Dependable Secure Comput., pp. 1–14, 2022.

Digital Library

[37]

T. Zhao, M. Yue, and J. Wang, “Robust power system stability assessment against adversarial machine learning-based cyberattacks via online purification,” IEEE Trans. Power Syst., vol. 38, no. 6, pp. 5613–5622, Nov. 2023.

[38]

M. Wu, R. Roy, P. Serna Torre, and P. Hidalgo-Gonzalez, “Effectiveness of learning algorithms with attack and defense mechanisms for power systems,” Electric Power Syst. Res., vol. 212, 2022, Art. no.

[39]

Y. Wu and J. Wei-Kocsis, “A practical and stealthy adversarial attack for cyber-physical applications,” in Proc. AAAI-22 Workshop Adversarial Mach. Learn. Beyond, 2022, pp. 1–8.

[40]

J. Li, Y. Yang, J. S. Sun, K. Tomsovic, and H. Qi, “Towards adversarial-resilient deep neural networks for false data injection attack detection in power grids,” in Proc. 32nd Int. Conf. Comput. Commun. Netw., 2023, pp. 1–10.

[41]

T. Liu and T. Shu, “On the security of ANN-based AC state estimation in smart grid,” Comput. Secur., vol. 105, 2021, Art. no.

[42]

Q. Song, H. Jin, X. Huang, and X. Hu, “Multi-label adversarial perturbations,” in Proc. IEEE Int. Conf. Data Mining, 2018, pp. 1242–1247.

[43]

N. Zhou, W. Luo, X. Lin, P. Xu, and Z. Zhang, “Generating multi-label adversarial examples by linear programming,” in Proc. Int. Joint Conf. Neural Netw., 2020, pp. 1–8.

[44]

N. Zhou, W. Luo, J. Zhang, L. Kong, and H. Zhang, “Hiding all labels for multi-label images: An empirical study of adversarial examples,” in Proc. Int. Joint Conf. Neural Netw., 2021, pp. 1–8.

[45]

S. Hu, L. Ke, X. Wang, and S. Lyu, “T_kML-AP: Adversarial attacks to top-k multi-label learning,” in Proc. IEEE/CVF Int. Conf. Comput. Vis., 2021, pp. 7629–7637.

[46]

Z. Yang, Y. Han, and X. Zhang, “Characterizing the evasion attackability of multi-label classifiers,” in Proc. AAAI Conf. Artif. Intell., 2021, pp. 10647–10655.

[47]

L. Kong, W. Luo, H. Zhang, Y. Liu, and Y. Shi, “Evolutionary multilabel adversarial examples: An effective black-box attack,” IEEE Trans. Artif. Intell., vol. 4, no. 3, pp. 562–572, Jun. 2023.

[48]

S. Melacci et al., “Domain knowledge alleviates adversarial attacks in multi-label classifiers,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 12, pp. 9944–9959, Dec. 2022.

[49]

J. Jia, W. Qu, and N. Gong, “Multiguard: Provably robust multi-label classification against adversarial examples,” in Proc. Adv. Neural Inf. Process. Syst., 2022, pp. 10150–10163.

[50]

Y. Liu, P. Ning, and M. K. Reiter, “False data injection attacks against state estimation in electric power grids,” ACM Trans. Inf. Syst. Secur., vol. 14, no. 1, 2011, Art. no.

[51]

A. Abur and A. G. Exposito, Power System State Estimation: Theory and Implementation. Boca Raton, FL, USA: CRC Press, 2004.

[52]

B. Gao and L. Pavel, “On the properties of the softmax function with application in game theory and reinforcement learning,” 2017,.

[53]

M.-L. Zhang and Z.-H. Zhou, “A review on multi-label learning algorithms,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 8, pp. 1819–1837, Aug. 2014.

[54]

N. Baracaldo and J. Joshi, “An adaptive risk management and access control framework to mitigate insider threats,” Comput. Secur., vol. 39, pp. 237–254, 2013.

Digital Library

[55]

R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, “MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 12–19, Feb. 2011.

[56]

S. Ahmed, Y. Lee, S. Hyun, and I. Koo, “Unsupervised machine learning-based detection of covert data integrity assault in smart grid networks utilizing isolation forest,” IEEE Trans. Inf. Forensics Security, vol. 14, no. 10, pp. 2765–2777, Oct. 2019.

Digital Library

[57]

M. Esmalifalak, L. Liu, N. Nguyen, R. Zheng, and Z. Han, “Detecting stealthy false data injection using machine learning in smart grid,” IEEE Syst. J., vol. 11, no. 3, pp. 1644–1652, Sep. 2017.

[58]

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in Proc. 6th Int. Conf. Learn. Representations, 2018, pp. 1–23.

[59]

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” 2014,.

[60]

N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proc. ACM on Asia Conf. Comput. Commun. Secur., 2017, pp. 506–519.

[61]

A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” 2016,.

[62]

F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel, “Ensemble adversarial training: Attacks and defenses,” 2017,.

[63]

N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in Proc. IEEE Symp. Secur. Privacy, 2016, pp. 582–597.

Cited By

Lyu QXie HWang WCheng YChen YWang Z(2024)TFANComputers and Security10.1016/j.cose.2024.103980144:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.cose.2024.103980
Liu JCui ZZhou Y(2024)Mitigating dimension constraintsComputers and Electrical Engineering10.1016/j.compeleceng.2024.109355118:PAOnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109355

Index Terms

LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection
1. Computing methodologies
2. Social and professional topics

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cover image IEEE Transactions on Dependable and Secure Computing

IEEE Transactions on Dependable and Secure Computing Volume 21, Issue 5

Sept.-Oct. 2024

750 pages

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1545-5971 © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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IEEE Computer Society Press

Washington, DC, United States

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Published: 12 January 2024

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Cited By

Lyu QXie HWang WCheng YChen YWang Z(2024)TFANComputers and Security10.1016/j.cose.2024.103980144:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.cose.2024.103980
Liu JCui ZZhou Y(2024)Mitigating dimension constraintsComputers and Electrical Engineering10.1016/j.compeleceng.2024.109355118:PAOnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109355

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