Wavelet Support Vector Machine Algorithm in Power Analysis Attacks

Hou, S.; Zhou, Yajin; Liu, H.; Zhu, Na

doi:10.13164/re.2017.0890

Cited by 26 publications

(24 citation statements)

References 42 publications

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“…The authors studied ML algorithms mostly using 9 or up to 16 classes. We successfully recovered the secret key by using SVM in [16]. These related contributions suggest that some ML algorithms are effective in PA attacks.…”

Section: Introductionmentioning

confidence: 81%

“…The DPA Contest v4 (DPACv4) [24] provides 100,000 power traces of the masked AES software implementation. Since the mask value is known in [16], we can directly convert this dataset to an unprotected scenario. We selected 4000 (DS0) and 8000 (DS1) random power traces to make a fair comparison of all experiments.…”

Section: Methodologiesmentioning

confidence: 99%

“…In recent years, a large number of ML algorithms have been applied to PA attacks, e.g. multilayer perceptron [7], [8], k-means clustering [9], k-nearest neighbors [10], support vector machine (SVM) [11][12][13][14][15][16], etc. Hospodar et al [11] first applied SVM to PA attacks.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the performance of SVM is slightly superior to other ML algorithms. However, almost all studies on SVM in PA attacks [12][13][14][15][16] assume power traces are sufficient to reveal key-related leakage information and the number of profiling traces belonging to each class is equal. Indeed, the number of profiling traces may be different in the real attack scenario.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting Support Vector Machine Algorithm to Break the Secret Key

Hou¹,

Zhou²,

Liu³

et al. 2018

RADIOENGINEERING

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Template attacks (TA) and support vector machine (SVM) are two effective methods in side channel attacks (SCAs). Almost all studies on SVM in SCAs assume the required power traces are sufficient, which also implies the number of profiling traces belonging to each class is equivalent. Indeed, in the real attack scenario, there may not be enough power traces due to various restrictions. More specifically, the Hamming Weight of the S-Box output results in 9 binomial distributed classes, which significantly reduces the performance of SVM compared with the uniformly distributed classes. In this paper, the impact of the distribution of profiling traces on the performance of SVM is first explored in detail. And also, we conduct Synthetic Minority Oversampling TEchnique (SMOTE) to solve the problem caused by the binomial distributed classes. By using SMOTE, the success rate of SVM is improved in the testing phase, and SVM requires fewer power traces to recover the key. Besides, TA is selected as a comparison. In contrast to what is perceived as common knowledge in unrestricted scenarios, our results indicate that SVM with proper parameters can significantly outperform TA.

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Section: Introductionmentioning

confidence: 81%

Section: Methodologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting Support Vector Machine Algorithm to Break the Secret Key

Hou¹,

Zhou²,

Liu³

et al. 2018

RADIOENGINEERING

Self Cite

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“…Chose ( +1)mod8 (19) go to Line (8) (20) end if (21) end if there was one obvious peak in original DPA of DES algorithm for each Sbox. On the contrary, several peaks in our scheme with 5000 traces we found in Figure 5 were "ghost" peaks, which leads to wrong key corresponding to the target Sbox.…”

Section: Inputmentioning

confidence: 99%

A New Type of Countermeasure against DPA in Multi‐Sbox of Block Cipher

Zhang

Zhong

2018

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) provides the network for physical devices, like home appliances, embedded with electronics, sensors, and software, to share and exchange data. With its fast development, security of IoT has become a crucial problem. Among the methods of attack, side-channel attack has proven to be an effective tool to compromise the security of different devices with improving techniques of data processing, like DPA and CPA. Meanwhile, many countermeasures have risen accordingly as well, such as masking and noise addition. However, their common deficiency was that every single countermeasure might not be able to protect the key information completely after statistical analysis. Sensitive information will be disclosed during differential power analysis of Sbox, since it is the only nonlinear component in block cipher. Thus, how to protect Sbox effectively was the highlight of researches. Based on Sbox-reuse concept proposed by Bilgin, this paper put forward a new type of a countermeasure scheme against DPA in multi-Sbox of block cipher. We first converted the multi-Sbox into 4 × 4 permutations and then reused permutation with the algebraic degree of more than one so as to turn it into a special reusable Sbox and then numbered 4 × 4 permutation input. Finally, we made these inputs of permutations completely random by masking. Since it was necessary to make the collected power consumption curve subject to alignment process in DPA by chosen-plaintext attack, this scheme combined the concept from DPA countermeasures of masking and noise addition. After the experiment with the proposed implementation, successful prevention of the attacker from accurately aligning the power consumption curve of the target Sbox has been proven, and the level of security has been improved by adding more random noise to protect key information and decrease the accuracy of statistical analysis.

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Machine Learning for Hardware Security: Opportunities and Risks

Elnaggar

Chakrabarty

2018

J Electron Test

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Wavelet Support Vector Machine Algorithm in Power Analysis Attacks

Cited by 26 publications

References 42 publications

Exploiting Support Vector Machine Algorithm to Break the Secret Key

Exploiting Support Vector Machine Algorithm to Break the Secret Key

A New Type of Countermeasure against DPA in Multi‐Sbox of Block Cipher

Machine Learning for Hardware Security: Opportunities and Risks

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