Generation of False Data Injection Attacks using Conditional Generative Adversarial Networks

Mohammadpourfard, Mostafa; Ghanaatpishe, Fateme; Mohammadi, Marziyeh; Lakshminarayana, Subhash; Pechenizkiy, Mykola

doi:10.1109/isgt-europe47291.2020.9248967

Cited by 17 publications

(12 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The claim is proved. Thus, ( 40) implies (39). By using 2 observer in (7), the attack-free state estimation error xi −x i 2 converges to zero as i goes infinity.…”

Section: Resilient Estimatormentioning

confidence: 98%

“…By using 2 observer in (7), the attack-free state estimation error xi −x i 2 converges to zero as i goes infinity. According to (39), xi − x i 2 also goes to zero in the limit.…”

Section: Resilient Estimatormentioning

confidence: 99%

“…Model-based attack generators such as [17], [38] generally adopt optimization-based mechanisms which are computationally expensive, even NP-hard. Most traditional data-driven attack generators [39], [40], in literature, still require the attack examples with labels for training. In this paper, we proposed an unsupervised generative model (GM) trained by multiple physics-based discriminators derived from the plant model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Algorithm Design for Resilient Cyber-Physical Systems using an Automated Attack Generative Model

Zheng¹,

Sayghe²,

Anubi³

2021

Preprint

View full text Add to dashboard Cite

<div>This paper presents a suite of algorithms for detecting and localizing attacks in cyber-physical systems, and performing improved resilient state estimation through a pruning algorithm. High performance rates for the underlying detection and localization algorithms are achieved by generating training data that cover large region of the attack space. An unsupervised generative model trained by physics-based discriminators is designed to generate successful false data injection attacks. Then the generated adversarial examples are used to train a multi-class deep neural network which detects and localizes the attacks on measurements. Next, a pruning algorithm is included to improve the precision of localization result and provide performance guarantees for the resulting resilient observer. The performance of the proposed method is validated using the numerical simulation of a water distribution cyber-physical system.</div>

show abstract

“…The claim is proved. Thus, ( 40) implies (39). By using 2 observer in (7), the attack-free state estimation error xi −x i 2 converges to zero as i goes infinity.…”

Section: Resilient Estimatormentioning

confidence: 98%

“…By using 2 observer in (7), the attack-free state estimation error xi −x i 2 converges to zero as i goes infinity. According to (39), xi − x i 2 also goes to zero in the limit.…”

Section: Resilient Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Algorithm Design for Resilient Cyber-Physical Systems using an Automated Attack Generative Model

Zheng¹,

Sayghe²,

Anubi³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The work in [27] presented a zero-parameter information attack that only requires power system's topology information. The works in [28], [29] employed machine learning techniques to carry out a FDIA. Specifically, they trained a generative adversarial network (GAN) to generate tampered power system measurements that will be stealthy with high probability.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, they trained a generative adversarial network (GAN) to generate tampered power system measurements that will be stealthy with high probability. While the works in [28], [29] and our work use generative adversarial networks (GANs) to carry out a FDIA, our approach has some important differences. Both works in [28], [29] use the DC linear power flow model.…”

Section: Introductionmentioning

confidence: 99%

Attack Power System State Estimation by Implicitly Learning the Underlying Models

Costilla-Enriquez

Weng

2023

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

False data injection attacks (FDIAs) are a real and latent threat in modern power systems networks due to the unprecedented integration of data acquisition systems. It is of utmost importance to understand attacking mechanisms to design countermeasures. To successfully deploy a FDIA, most past FDIA strategies need privileged power system information, which is carefully held by the power system operator. Newer approaches circumvent this issue by solely relying on intercepted measurement data, but they lack mathematical warranties of succeeding. This paper exposes power systems' vulnerability by showing that it is possible to deploy an attack without confidential information and, at the same time, to have a high probability of being successful. We present a scheme that learns (1) the implicit power system measurement distribution and (2) a surrogate of the unknown state estimator model. The proposed framework utilizes a Wasserstein generative adversarial network to learn the measurement distribution and an autoencoder to learn the unknown state estimator model. Additionally, we present a convergence proof that ensures that the proposed framework converges to the power system measurement distribution. The proposed method is demonstrated to be successful via extensive simulation on IEEE 9-, 14-, 57-, 118-, and 300-bus test cases.

show abstract

Discriminating Deceptive Energy Generation of Photovoltaic Systems by Deep Learning and Adversarial Networks

Polo-Rodriguez,

Almonacid-Olleros,

Almonacid

et al. 2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Generation of False Data Injection Attacks using Conditional Generative Adversarial Networks

Cited by 17 publications

References 9 publications

Algorithm Design for Resilient Cyber-Physical Systems using an Automated Attack Generative Model

Algorithm Design for Resilient Cyber-Physical Systems using an Automated Attack Generative Model

Attack Power System State Estimation by Implicitly Learning the Underlying Models

Discriminating Deceptive Energy Generation of Photovoltaic Systems by Deep Learning and Adversarial Networks

Contact Info

Product

Resources

About