A machine learning approach against a masked AES

Lerman, Liran; Bontempi, Gianluca; Markowitch, Olivier

doi:10.1007/s13389-014-0089-3

Cited by 109 publications

(40 citation statements)

References 25 publications

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“…In another research direction, SVM was recently used as a tool to exploit device security. The results in [129,130] showed that ML methods can break cryptographic devices and that SVM is more effective in breaking cryptographic devices than the traditional method (i.e. template attack).…”

Section: ) Support Vector Machines (Svms)mentioning

confidence: 99%

“…Recent advances in ML and DL algorithms have enabled them to be used in breaking cryptographic implementations. For example, two previous studies [129,130] used ML to break cryptographic systems using SVMs, which outperformed the template attack. Similarly, the authors in [189] investigated different DL algorithms to break cryptographic systems and reported that DL can break cryptographic systems.…”

Section: ML and Dl Challenges 1) Possible Misuse Of Ml And Dl Algomentioning

confidence: 99%

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A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

780

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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Section: ) Support Vector Machines (Svms)mentioning

confidence: 99%

Section: ML and Dl Challenges 1) Possible Misuse Of Ml And Dl Algomentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

780

333

View full text Add to dashboard Cite

show abstract

“…Similarly, SCA using machine learning techniques, such as k ‐means clustering, support vector machine, and random forest, also requires pre‐processing and POI selection. As pre‐processing and POI selection require human engineering, they significantly impact the results .…”

Section: Deep Learning‐based Side‐channel Analysismentioning

confidence: 99%

Recent advances in deep learning‐based side‐channel analysis

Jin

Kim

et al. 2020

ETRI Journal

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As side‐channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side‐channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning‐based side‐channel analysis. In particular, we outline how deep learning is applied to side‐channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

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“…But these ML-based approaches mentioned above rely on the assumption that the leakage profile from the profiling and the target devices are similar. (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)), running an AES encryption operation for a fixed plaintext and a fixed subkey 0x00 for first keybyte. Amplitudes are reported in Arbitrary Unit (A.U.…”

Section: Introduction a Motivationmentioning

confidence: 99%

Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack

Golder

Das

Danial

et al. 2019

IEEE Trans. VLSI Syst.

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Power side-channel analysis (SCA) has been of immense interest to most embedded designers to evaluate the physical security of the system. This work presents profiling-based cross-device power SCA attacks using deep learning techniques on 8-bit AVR microcontroller devices running AES-128. Firstly, we show the practical issues that arise in these profiling-based cross-device attacks due to significant device-to-device variations. Secondly, we show that utilizing Principal Component Analysis (PCA) based pre-processing and multi-device training, a Multi-Layer Perceptron (MLP) based 256-class classifier can achieve an average accuracy of 99.43% in recovering the first key byte from all the 30 devices in our data set, even in the presence of significant inter-device variations. Results show that the designed MLP with PCA-based pre-processing outperforms a Convolutional Neural Network (CNN) with 4-device training by ∼ 20% in terms of the average test accuracy of cross-device attack for the aligned traces captured using the ChipWhisperer hardware. Finally, to extend the practicality of these cross-device attacks, another preprocessing step, namely, Dynamic Time Warping (DTW) has been utilized to remove any misalignment among the traces, before performing PCA. DTW along with PCA followed by the 256-class MLP classifier provides ≥10.97% higher accuracy than the CNN based approach for cross-device attack even in the presence of up to 50 time-sample misalignments between the traces.

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A machine learning approach against a masked AES

Cited by 109 publications

References 25 publications

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Recent advances in deep learning‐based side‐channel analysis

Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack

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