Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices

Lee, Young Han; Jun, Suckjoon; Cho, Yungi; Han, Woorim; Moon, Hyungon; Paek, Yunheung

doi:10.1007/978-3-031-17143-7_18

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The application of deep learning techniques in side-channel analysis has shown that deep networks provide a more sophisticated analysis of signals and achieve better attack results compared to template attacks and traditional machine learning [29][30][31][32]. It is crucial to train and test on traces captured from different boards and using diverse implementations of the cryptographic algorithm under attack [33].…”

Section: Related Workmentioning

confidence: 99%

A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning

Cui,

Zhang,

Fang

et al. 2023

Applied Sciences

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The leakage signals, including electromagnetic, energy, time, and temperature, generated during the operation of password devices contain highly correlated key information, which leads to security vulnerabilities. In traditional encryption algorithms, the length of the key greatly affects the upper limit of its security against cracking. Regarding side-channel attacks on long-key algorithms, traditional template attack methods characterize the energy traces using multivariate Gaussian distribution during the template construction phase. The exhaustive key-guessing process is expected to consume a significant amount of time and computational resources. Therefore, to analyze the effectiveness of obtaining key values from the side information of password devices, we propose an innovative attack method based on a divide-and-conquer logical structure, targeting semi-bytes. We construct a collection of key classification submodules with symmetric correlations. By integrating a differential network model for byte-block sets and an end-to-end direct attack method, we form a holistic symmetric decision framework and propose a key classification structure based on deep transfer learning. This structure consists of three main parts: side information data acquisition, analysis of key-value effectiveness, and determination of attack positions. It employs multiple parallel symmetric subnetworks, effectively improving attack efficiency and reducing the key enumeration range. Experimental results show that the optimal attack accuracy of the network model can reach 91%, with an average attack accuracy of 78%. It overcomes overfitting issues under small sample dataset conditions.

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Section: Related Workmentioning

confidence: 99%

A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning

Cui,

Zhang,

Fang

et al. 2023

Applied Sciences

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Unlabeled learning algorithms and operations: overview and future trends in defense sector

e Oliveira,

Rodrigues,

Pereira

et al. 2024

Artif Intell Rev

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In the defense sector, artificial intelligence (AI) and machine learning (ML) have been used to analyse and decipher massive volumes of data, namely for target recognition, surveillance, threat detection and cybersecurity, autonomous vehicles and drones guidance, and language translation. However, there are key points that have been identified as barriers or challenges, especially related to data curation. For this reason, and also due to the need for quick response, the defense sector is looking for AI technologies capable of successfully processing and extracting results from huge amounts of unlabelled or very poorly labelled data. This paper presents an in-depth review of AI/ML algorithms for unsupervised or poorly supervised data, and machine learning operations (MLOps) techniques that are suitable for the defense industry. The algorithms are divided according to their nature, meaning that they either focus on techniques, or on applications. Techniques can belong to the supervision spectrum, or focus on explainability. Applications are either focused on text processing or computer vision. MLOps techniques, tools and practices are then discussed, revealing approaches and reporting experiences with the objective of declaring how to make the operationalization of ML integrated systems more efficient. Despite many contributions from several researchers and industry, further efforts are required to construct substantially robust and reliable models and supporting infrastructures for AI systems, which are reliable and suitable for the defense sector. This review brings up-to-date information regarding AI algorithms and MLOps that will be helpful for future research in the field.

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Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices

Cited by 2 publications

References 15 publications

A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning

A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning

Unlabeled learning algorithms and operations: overview and future trends in defense sector

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