Cyber Attacks on Smart Energy Grids Using Generative Adverserial Networks

Ahmadian, Saeed; Malki, Heidar; Han, Zhu

doi:10.1109/globalsip.2018.8646424

Cited by 25 publications

(12 citation statements)

References 15 publications

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“…In another research,the attacker has two goals: a) to be undetected by the system defender and b) to optimize the false data injection attack into a smart grid [174] . To achieve these objectives, the authors based this attack on generative adversarial network (GAN).…”

Section: Smart Attacks Based On Data Generationmentioning

confidence: 99%

How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey

Bout

Loscrì

Gallais

2022

IEEE Commun. Surv. Tutorials

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Section: Smart Attacks Based On Data Generationmentioning

confidence: 99%

How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey

Bout

Loscrì

Gallais

2022

IEEE Commun. Surv. Tutorials

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“…In Bot-GAN, the generative model is used to improve the amount of data to bypass the original IDS acting as a discriminator. In [34], the authors proposed a GAN-based framework to evaluate cyber-attacks on the smart energy grid. The framework employs a GAN generator to generate abnormal traffic to evaluate the existing system defender.…”

Section: Related Workmentioning

confidence: 99%

IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT

Nguyen

Tran

et al. 2022

Electronics

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The increasing popularity of the Internet of Things (IoT) has significantly impacted our daily lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for us; on the other hand, the devices are susceptible to various cyber-attacks due to the lack of solid security mechanisms and hardware security support. In this paper, we present IMIDS, an intelligent intrusion detection system (IDS) to protect IoT devices. IMIDS’s core is a lightweight convolutional neural network model to classify multiple cyber threats. To mitigate the training data shortage issue, we also propose an attack data generator powered by a conditional generative adversarial network. In the experiment, we demonstrate that IMIDS could detect nine cyber-attack types (e.g., backdoors, shellcode, worms) with an average F-measure of 97.22% and outperforms its competitors. Furthermore, IMIDS’s detection performance is notably improved after being further trained by the data generated by our attack data generator. These results demonstrate that IMIDS can be a practical IDS for the IoT scenario.

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“…Therefore, ML algorithms allowing to generate data (e.g., Generative Algorithm Network -GAN) can prove useful in this process. In [11], authors demonstrate that GAN can allow to generate similar data and intentionally deceive the system with a false injection attack. The final motivation for using ML algorithms when creating an attack is that they can be used to modify the behavior of an IoT network.…”

Section: Smart Attack In the Literaturementioning

confidence: 99%