A machine learning low‐dropout regulator‐assisted differential power analysis attack countermeasure with voltage scaling

Cheng, Jiafeng; Liu, Wenrui; Sun, Nengyuan; Sun, Caiban; Wang, Chunyang; Bi, Yijian; Wen, Yiming; Zhang, Hongliu; Zhang, Pengcheng; Yu, Weize

doi:10.1002/cta.3583

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Power analysis attacks are side-channel attacks that exploit the power consumption of a device to infer sensitive information, such as encryption keys, passwords, or user activities [162]. These attacks can be classified into two major categories as (1) simple power analysis (SPA), which involves observing the power consumption of a device and identifying patterns or features that reveal information; and (2) differential power analysis (DPA), which involves collecting the multiple power traces of a device and applying statistical analysis to extract information.…”

Section: Power Analysis Attacksmentioning

confidence: 99%

Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses

et al. 2023

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There is an exponential rise in the use of smartphones in government and private institutions due to business dependencies such as communication, virtual meetings, and access to global information. These smartphones are an attractive target for cybercriminals and are one of the leading causes of cyber espionage and sabotage. A large number of sophisticated malware attacks as well as advanced persistent threats (APTs) have been launched on smartphone users. These attacks are becoming significantly more complex, sophisticated, persistent, and undetected for extended periods. Traditionally, devices are targeted by exploiting a vulnerability in the operating system (OS) or device sensors. Nevertheless, there is a rise in APTs, side-channel attacks, sensor-based attacks, and attacks launched through the Google Play Store. Previous research contributions have lacked contemporary threats, and some have proven ineffective against the latest variants of the mobile operating system. In this paper, we conducted an extensive survey of papers over the last 15 years (2009–2023), covering vulnerabilities, contemporary threats, and corresponding defenses. The research highlights APTs, classifies malware variants, defines how sensors are exploited, visualizes multiple ways that side-channel attacks are launched, and provides a comprehensive list of malware families that spread through the Google Play Store. In addition, the research provides details on threat defense solutions, such as malware detection tools and techniques presented in the last decade. Finally, it highlights open issues and identifies the research gap that needs to be addressed to meet the challenges of next-generation smartphones.

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Section: Power Analysis Attacksmentioning

confidence: 99%

Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses

et al. 2023

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“…DL-SCA with unsupervised learning is suitable for scenarios where the key is unknown and a substantial volume of unlabeled power traces are available [5][6][7]. Although this type of DL-SCA exhibits significant advantages over traditional non-profiling SCAs such as DPA [8] and CPA [9], it still presents certain gaps when compared to DL-SCA with supervised learning. DL-SCA with semi-supervised learning overcomes the limitations of the previous two types of DL-SCA, enabling key recovery even when labeled power traces are scarce.…”

Section: Introductionmentioning

confidence: 99%

Improve the Performance of Semi-Supervised Side-channel Analysis Using HWFilter Method

2024

KSII TIIS

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Side-channel analysis (SCA) is a cryptanalytic technique that exploits physical leakages, such as power consumption or electromagnetic emanations, from cryptographic devices to extract secret keys used in cryptographic algorithms. Recent studies have shown that training SCA models with semi-supervised learning can effectively overcome the problem of few labeled power traces. However, the process of training SCA models using semi-supervised learning generates many pseudo-labels. The performance of the SCA model can be reduced by some of these pseudo-labels. To solve this issue, we propose the HWFilter method to improve semisupervised SCA. This method uses a Hamming Weight Pseudo-label Filter (HWPF) to filter the pseudo-labels generated by the semi-supervised SCA model, which enhances the model's performance. Furthermore, we introduce a normal distribution method for constructing the HWPF. In the normal distribution method, the Hamming weights (HWs) of power traces can be obtained from the normal distribution of power points. These HWs are filtered and combined into a HWPF. The HWFilter was tested using the ASCADv1 database and the AES_HD dataset. The experimental results demonstrate that the HWFilter method can significantly enhance the performance of semi-supervised SCA models. In the ASCADv1 database, the model with HWFilter requires only 33 power traces to recover the key. In the AES_HD dataset, the model with HWFilter outperforms the current best semi-supervised SCA model by 12%.

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A machine learning low‐dropout regulator‐assisted differential power analysis attack countermeasure with voltage scaling

Cited by 2 publications

References 26 publications

Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses

Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses

Improve the Performance of Semi-Supervised Side-channel Analysis Using HWFilter Method

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