On the Use of Independent Component Analysis to Denoise Side-Channel Measurements

Maghrebi, Houssem; Prouff, Emmanuel

doi:10.1007/978-3-319-89641-0_4

Cited by 11 publications

(5 citation statements)

References 49 publications

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“…It is clear from the literature that the ICA algorithms perform well as compare to adaptive filtering techniques like weiner filter, kalman filter etc . as shown in Mohammed, Hassan & Ferikoglu (2021) , Maghrebi & Prouff (2018) , Martinek et al (2021) , Maghrebi & Prouff (2018) , Uddin et al (2020) , and Villena et al (2018) . Mohammed, Hassan & Ferikoglu (2021) in particular mentioned that the ICA algorithm gives more accurate results than the extended kalman filter in reducing baseline wandering and electrode movement artifacts.…”

Section: Introductionmentioning

confidence: 78%

Isolation of multiple electrocardiogram artifacts using independent vector analysis

Uddin¹,

Altaf²,

Ahmad³

et al. 2023

PeerJ Computer Science

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Electrocardiogram (ECG) signals are normally contaminated by various physiological and nonphysiological artifacts. Among these artifacts baseline wandering, electrode movement and muscle artifacts are particularly difficult to remove. Independent component analysis (ICA) is a well-known technique of blind source separation (BSS) and is extensively used in literature for ECG artifact elimination. In this article, the independent vector analysis (IVA) is used for artifact removal in the ECG data. This technique takes advantage of both the canonical correlation analysis (CCA) and the ICA due to the utilization of second-order and high order statistics for un-mixing of the recorded mixed data. The utilization of recorded signals along with their delayed versions makes the IVA-based technique more practical. The proposed technique is evaluated on real and simulated ECG signals and it shows that the proposed technique outperforms the CCA and ICA because it removes the artifacts while altering the ECG signals minimally.

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Section: Introductionmentioning

confidence: 78%

Isolation of multiple electrocardiogram artifacts using independent vector analysis

Uddin¹,

Altaf²,

Ahmad³

et al. 2023

PeerJ Computer Science

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“…Besides, they did not deeply investigate whether their method can efficiently work in the case of analyzing random-delay countermeasures. To further optimize the performance of CPA attacks, Maghrebi and Prouff (2018) designed a practical Independent-Component Analysis (ICA) based framework to enhance the performance of CPA attacks. Compared with the previous work (Merino Del Pozo and Standaert 2015), their method allows reducing the number of data from 6000 to 2000 in the case of analyzing software-based unprotected implementation of AES.…”

Section: Related Workmentioning

confidence: 99%

Enhancing non-profiled side-channel attacks by time-frequency analysis

Jin,

Zhou

2023

Cybersecurity

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Side-channel analysis (SCA) has become an increasing important method to assess the physical security of cryptographic systems. In the process of SCA, the number of attack data directly determines the performance of SCA. With sufficient attack data, the adversary can achieve a successful SCA. However, in reality, the cryptographic device may be protected with some countermeasures to limit the number of encryptions using the same key. In this case, the adversary cannot use casual numbers of data to perform SCA. The performance of SCA will be severely dropped if the attack traces are insufficient. In this paper, we introduce wavelet scatter transform (WST) and short-time fourier transform (STFT) to non-profiled side-channel analysis domains, to improve the performance of side-channel attacks in the context of insufficient data. We design a practical framework to provide suitable parameters for WST/STFT-based SCA. Using the proposed method, the WST/STFT-based SCA method can significantly enhance the performance and robustness of non-profiled SCA. The practical attacks against four public datasets show that the proposed method is able to achieve more robust performance. Compared with the original correlation power analysis (CPA), the number of attack data can be reduced by 50–95%.

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“…Eventually, we notice that many papers study trace denoising, using typically wavelets [16], Independent Component Analysis (ICA) [29], etc. In our work, we exploit traces "raw", so as to highlight the sole impact of multivariate analysis on attack efficiency.…”

Section: State-of-the-artmentioning

confidence: 99%

On the power of template attacks in highly multivariate context

et al. 2020

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When implemented in software (or hardware), a cryptographic protocol can leak sensitive information during its execution. Side-channel attacks can use those leakages in order to reveal some information about the secret used by the algorithm. The leaking side-channel information can take place in many time samples. Measurement appliances can cope with the acquisition of multiple samples. From an adversarial point of view, it is therefore beneficial to attempt to make the most of highly multivariate traces. On the one hand, template attacks have been introduced to deal with multivariate leakages, with as few assumptions as possible on the leakage model. On the other hand, many works have underlined the need for dimensionality reduction. In this paper, we clarify the relationship between template attacks in full space and in linear subspaces, in terms of success rate. In particular, we exhibit a clear mathematical expression for template attacks, which enables an efficient computation even on large dimensions such as several hundred of samples. It is noteworthy that both of PoI-based and PCA

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On the Use of Independent Component Analysis to Denoise Side-Channel Measurements

Cited by 11 publications

References 49 publications

Isolation of multiple electrocardiogram artifacts using independent vector analysis

Isolation of multiple electrocardiogram artifacts using independent vector analysis

Enhancing non-profiled side-channel attacks by time-frequency analysis

On the power of template attacks in highly multivariate context

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