Innovative Method of the Power Analysis

This paper describes an innovative method of the power analysis which presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards. The proposed method analyzes power consumption of the AES (Advanced Encryption Standard) algorithm with neural network, which successively classifies the first byte of the secret key. This way of the power analysis is an entirely new approach and it is designed to combine the advantages of simple and differential power analysis. In the extreme case, this feature allows to determine the whole secret key of a cryptographic module only from one measured power trace. This attribute makes the proposed method very attractive for potential attackers. Besides theoretical design of the method, we also provide the first implementation results. We assume that the method will be certainly optimized to obtain more accurate classification results in the future

DLDDO: Deep Learning to Detect Dummy Operations

Recently, research on deep learning based side-channel analysis (DLSCA) has received a lot of attention. Deep learning-based profiling methods similar to template attacks as well as non-profiling-based methods similar to differential power analysis have been proposed. DLSCA methods have been proposed for targets to which masking schemes or jitter-based hiding schemes are applied. However, most of them are methods for finding the secret key, except for methods for preprocessing, and there are no studies on the target to which the dummy-based hiding schemes or shuffling schemes are applied. In this paper, we propose a DLSCA for detecting dummy operations. In the previous study, dummy operations were detected using the method called BCDC, but there is a disadvantage in that it is impossible to detect dummy operations for commercial devices such as an IC card. We consider the detection of dummy operations as a multi-label classification problem and propose a deep learning method based on CNN to solve it. As a result, it is possible to successfully perform detection of dummy operations on an IC card, which was not possible in the previous study

Convolutional Neural Networks with Data Augmentation against Jitter-Based Countermeasures.

International audienceIn the context of the security evaluation of cryptographic implementations, profiling attacks (aka Template Attacks) play a fundamental role. Nowadays the most popular Template Attack strategy consists in approximating the information leakages by Gaussian distributions. Nevertheless this approach suffers from the difficulty to deal with both the traces misalignment and the high dimensionality of the data. This forces the attacker to perform critical preprocessing phases, such as the selection of the points of interest and the realignment of measurements. Some software and hardware countermeasures have been conceived exactly to create such a misalignment. In this paper we propose an end-to-end profiling attack strategy based on the Convolutional Neural Networks: this strategy greatly facilitates the attack roadmap, since it does not require a previous trace realignment nor a precise selection of points of interest. To significantly increase the performances of the CNN, we moreover propose to equip it with the data augmentation technique that is classical in other applications of Machine Learning. As a validation, we present several experiments against traces misaligned by different kinds of countermeasures, including the augmentation of the clock jitter effect in a secure hardware implementation over a modern chip. The excellent results achieved in these experiments prove that Convolutional Neural Networks approach combined with data augmentation gives a very efficient alternative to the state-of-the-art profiling attacks

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Power analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards. In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models such as Support Vector Machine (SVM), RF (Random Forest) and Multi-Layer Perceptron (MLP). In this paper, we made an extensive comparison of machine learning algorithms in the power analysis. For this purpose, we implemented a verification program that always chooses the optimal settings of individual machine learning models in order to obtain the best classification accuracy. In our research, we used three datasets, the first containing the power traces of an unprotected AES (Advanced Encryption Standard) implementation. The second and third datasets are created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4). The obtained results revealed some interesting facts, namely, an elementary k-NN (k-Nearest Neighbors) algorithm, which has not been commonly used in power analysis yet, shows great application potential in practice

Introduction of Cyber Arena

This article describes the current state of a private cyber range named Cyber Arena that is developed by our university. First chapter briefly explains the current problematic and describes the sequence diagram of exemplary communication between the main Cyber Arena components that are OpenStack, ansible AWX, and web-based Training application. Second chapter focuses on Arena architecture and the following chapters briefly explain all mentioned main Arena components

Location, Location, Location: Revisiting Modeling and Exploitation for Location-Based Side Channel Leakages

Contains fulltext : 215019.pdf (Publisher’s version ) (Open Access)Advances in Cryptology - ASIACRYPT 2019 - 25th International Conference on the Theory and Application of Cryptology and Information Security, Kobe, Japan, December 8-12, 201