TS3FCM: trusted safe semi-supervised fuzzy clustering method for data partition with high confidence

Huan, Phung The; Thong, Pham Huy; Tuấn, Trần Mạnh; Hop, Dang Trong; Thái, Vũ Đức; Minh, Nguyen Hai; Giang, Nguyễn Long; Sơn, Lê Hoàng

doi:10.1007/s11042-022-12133-6

Cited by 10 publications

(1 citation statement)

References 34 publications

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“…Correct labels can enhance the model's performance, while incorrect ones can degrade it, potentially to the extent that the model fails to recover the key. A common solution is to add a confidence level during the training phase [19][20][21]. This solution is only applicable to models with two categories.…”

Section: Introductionmentioning

confidence: 99%

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

2024

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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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Section: Introductionmentioning

confidence: 99%