Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866929211132870656 |
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| author | Gu, Ruizhe Wang, Ping Zheng, Mengce Hu, Honggang Yu, Nenghai |
| author_facet | Gu, Ruizhe Wang, Ping Zheng, Mengce Hu, Honggang Yu, Nenghai |
| contents | Numerous previous works have studied deep learning algorithms applied in the context of side-channel attacks, which demonstrated the ability to perform successful key recoveries. These studies show that modern cryptographic devices are increasingly threatened by side-channel attacks with the help of deep learning. However, the existing countermeasures are designed to resist classical side-channel attacks, and cannot protect cryptographic devices from deep learning based side-channel attacks. Thus, there arises a strong need for countermeasures against deep learning based side-channel attacks. Although deep learning has the high potential in solving complex problems, it is vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrectly.
In this paper, we propose a kind of novel countermeasures based on adversarial attacks that is specifically designed against deep learning based side-channel attacks. We estimate several models commonly used in deep learning based side-channel attacks to evaluate the proposed countermeasures. It shows that our approach can effectively protect cryptographic devices from deep learning based side-channel attacks in practice. In addition, our experiments show that the new countermeasures can also resist classical side-channel attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2009_10568 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks Gu, Ruizhe Wang, Ping Zheng, Mengce Hu, Honggang Yu, Nenghai Cryptography and Security Numerous previous works have studied deep learning algorithms applied in the context of side-channel attacks, which demonstrated the ability to perform successful key recoveries. These studies show that modern cryptographic devices are increasingly threatened by side-channel attacks with the help of deep learning. However, the existing countermeasures are designed to resist classical side-channel attacks, and cannot protect cryptographic devices from deep learning based side-channel attacks. Thus, there arises a strong need for countermeasures against deep learning based side-channel attacks. Although deep learning has the high potential in solving complex problems, it is vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrectly. In this paper, we propose a kind of novel countermeasures based on adversarial attacks that is specifically designed against deep learning based side-channel attacks. We estimate several models commonly used in deep learning based side-channel attacks to evaluate the proposed countermeasures. It shows that our approach can effectively protect cryptographic devices from deep learning based side-channel attacks in practice. In addition, our experiments show that the new countermeasures can also resist classical side-channel attacks. |
| title | Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2009.10568 |