Side-channel Analysis Methods Research Articles

Enhancing non-profiled side-channel attacks by time-frequency analysis

Side-channel analysis (SCA) has become an increasing important method to assess the physical security of cryptographic systems. In the process of SCA, the number of attack data directly determines the performance of SCA. With sufficient attack data, the adversary can achieve a successful SCA. However, in reality, the cryptographic device may be protected with some countermeasures to limit the number of encryptions using the same key. In this case, the adversary cannot use casual numbers of data to perform SCA. The performance of SCA will be severely dropped if the attack traces are insufficient. In this paper, we introduce wavelet scatter transform (WST) and short-time fourier transform (STFT) to non-profiled side-channel analysis domains, to improve the performance of side-channel attacks in the context of insufficient data. We design a practical framework to provide suitable parameters for WST/STFT-based SCA. Using the proposed method, the WST/STFT-based SCA method can significantly enhance the performance and robustness of non-profiled SCA. The practical attacks against four public datasets show that the proposed method is able to achieve more robust performance. Compared with the original correlation power analysis (CPA), the number of attack data can be reduced by 50–95%.

The research of the side-channel analysis method based on deep learning for trusted platform module

At present, the trusted platform module (TPM) has been widely used in electricity, finance, transportation, and other industries, and its security has attracted much attention. The research on the side-channel attack (SCA) can be conducive to improving the security design technology of trusted platform modules. The side channel attack on the trusted platform module is studied from the initial differential power analysis (DPA) to the machine learning method. This paper introduces the deep learning techniques in SCA and proposes a new SCA technique based on deep learning. Firstly, on basis of the characteristics of SCA on TPM, we improve the convolutional neural network (CNN) models, including ResNet, VGG, and Google Net. Then, for the SM4 cipher implemented with and without countermeasures, we implement some attack experiments by exploiting the SCA based on various deep learning models and compare the experimental results with the DPA attack. The experiment results show that under the same circumstance, the SCA based on the deep learning method is more effective. We compare the effects of different CNN models and hyper-parameters to provide the basis and data for further research.

Breaking real-world COTS USIM cards with unknown side-channel countermeasures

In this paper, we examine the question of how to perform an efficient side-channel analysis (SCA) of real-world crypto products with proprietary side-channel countermeasures. Concretely, answering this question requires a suitable selection of side-channel analysis methods and various steps to instantiate it to break various side-channel countermeasures. We take the analysis of a protected prototype system as an example, to discuss how to efficiently use traditional SCA methods and Deep-Learning based SCA methods in the context of analyzing current side-channel countermeasures. Then, we extend the analysis research on a real-world commercial off-the-shelf (COTS) 4G Universal Subscriber Identity Module (USIM) card, showing how to reverse-engineer the embedded side-channel countermeasures by using side-channel information only. The experiment results show that the secret key and other operator-related parameters can be fully recovered without prior knowledge of side-channel countermeasures. This research allows us to provide a living case study of the physical security analysis of real-world crypto products, which might be worthy of more in-depth investigations.

Intensive Analysis of Physical Parameters of Power Sensors for Remote Side-Channel Attacks

Side-channel analysis methods can reveal the secret information of digital electronic systems by analyzing the dependency between the power consumption of implemented cryptographic algorithms and the secret data. Recent studies show that it is possible to gather information about power consumption from FPGAs without any physical access. High flexibilities of modern FPGAs cause that they are used for cloud accelerator in Platform as a Service (PaaS) system; however, new serious vulnerabilities emerged for these platforms. Although there are some reports about how switching activities from one region of FPGA affect other regions, details of this technique are not analyzed. In this paper, we analyzed the strength of this kind of attack and examined the impact of geometrical and electrical parameters of the victim/attacker modules on the efficiency of this attack. We utilized a Zynq-based Xilinx platform as the device under attack. Experimental results and analyses show that the distance between the victim module and the sensor modules is not the only effective parameter on the quality of attack; the influence of the relational location of victim/attacker modules could be more considerable on the quality of attack.

A Multiple Sieve Approach Based on Artificial Intelligent Techniques and Correlation Power Analysis

Side-channel analysis achieves key recovery by analyzing physical signals generated during the operation of cryptographic devices. Power consumption is one kind of these signals and can be regarded as a multimedia form. In recent years, many artificial intelligence technologies have been combined with classical side-channel analysis methods to improve the efficiency and accuracy. A simple genetic algorithm was employed in Correlation Power Analysis (CPA) when apply to cryptographic algorithms implemented in parallel. However, premature convergence caused failure in recovering the whole key, especially when plenty of large S-boxes were employed in the target primitive, such as in the case of AES. In this article, we investigate the reason of premature convergence and propose a Multiple Sieve Method (MS-CPA), which overcomes this problem and reduces the number of traces required in correlation power analysis. Our method can be adjusted to combine with key enumeration algorithms and further improves the efficiency. Simulation experimental results depict that our method reduces the required number of traces by and , compared to classic CPA and the Simple-Genetic-Algorithm-based CPA (SGA-CPA), respectively, when the success rate is fixed to . Real experiments performed on SAKURA-G confirm that the number of traces required for recovering the correct key in our method is almost equal to the minimum number that makes the correlation coefficients of correct keys stand out from the wrong ones and is much less than the numbers of traces required in CPA and SGA-CPA. When combining with key enumeration algorithms, our method has better performance. For the traces number being 200 (noise standard deviation ), the attacks success rate of our method is , which is much higher than the classic CPA with key enumeration ( success rate). Moreover, we adjust our method to work on that DPA contest v1 dataset and achieve a better result (40.04 traces) than the winning proposal (42.42 traces).

Gini-Impurity Index Analysis

In the past few decades, DPA-based side-channel attack strategies, such as DPA and CPA, have shown strong ability to analyze the security of the cryptographic implementations. However, the unpredictability of the leakage model and the correspondence between leakage behavior of the target device and the hypothetical leakage value make it less-effective without prior knowledge. Therefore, in this paper, we present a novel generic side-channel analysis method called Gini-impurity Index Analysis (GIA), utilizing Gini-impurity Index as the distinguisher, which can perform well even without any leakage model and is not sensitive to the existing methods’ restrictions about the leakage behavior. Firstly, we introduce the basic idea of GIA. According to the proposed GIA attack strategy, the Gini-impurity index for each key hypothesis should be calculated, determined by the clustered power consumption and the classified subsets based on the key dependent target function. Secondly, we verify the feasibility and evaluate the efficiency of GIA with different target functions by the practical experimental results against AES-128 implemented on an AT89S52 microcontroller. We present one possible multivariate extension of GIA and find the advantage of GIA on leakage information utilization. Thirdly, we present the results of comparisons. On the one hand, we compare GIA with three widely-used distinguishers under simulated traces in various leakage scenarios and practical traces with Hamming-weight-related leakage. Results confirm that GIA can always perform well with different leakage models in most situations. On the other hand, we analyze the relationship between GIA and Mutual Information Analysis (MIA). Theoretical and experimental results confirm that these two methods can obtain similar attack results. However, the guessing entropy of GIA is lower than MIA by up to 21%, and the averaged computational time overhead of GIA is lower than MIA by up to 13.3%, indicating that GIA is more efficient than MIA. Compared to traditional MIA, GIA is easier to operate and more flexible with noise. Therefore, GIA is an efficient and useful alternative to these existed strategies.

Improved Differential Fault Attack on LEA by Algebraic Representation of Modular Addition

Recently, as the number of IoT (Internet of Things) devices has increased, the use of lightweight cryptographic algorithms that are suitable for environments with scarce resources has also increased. Consequently, the safety of such cryptographic algorithms is becoming increasingly important. Among them, side-channel analysis methods are very realistic threats. In this paper, we propose a novel differential fault attack method on the Lightweight Encryption Algorithm (LEA) cipher which became the ISO/IEC international standard lightweight cryptographic algorithm in 2019. Previously proposed differential fault attack methods on the LEA used the Single Bit Flip model, making it difficult to apply to real devices. The proposed attack method uses a more realistic attacker assumption, the Random Word Error model. We demonstrate that the proposed attack method can be implemented on real devices using an electromagnetic fault injection setup. Our attack method has the weakest attacker assumption among attack methods proposed to date. In addition, the number of required fault-injected ciphertexts and the number of key candidates for which exhaustive search is performed are the least among all existing methods. Therefore, when implementing the LEA cipher on IoT deivces, designers must apply appropriate countermeasures against fault injection attacks.

An efficient multi-parameter approach for FPGA hardware Trojan detection

Hardware Trojan (HT) detection in the wild is a challenging endeavor since the inspector cannot have access to “golden chips” or special and trusted test measurements in order to be assisted in his assessment. Apart from detection, a HT analyzer sometimes needs to be able to understand the behavior of a HT, its activation principles and preconditions. In this paper an FPGA based HT detection and analysis methodology is proposed that uses multiple parameter processing in order to detect a HT and analyze its behavior without the presence of “golden” chip or measurements. The methodology introduces a series of scientifically sound stages to be followed in order to refine the HT detection process and proposes the scientific interconnection between these steps that makes this refinement possible. More specifically, in the paper, we propose the appropriate combination of a logic testing method, a run-time method and a side-channel analysis method to structure the proposed methodology and we apply this methodology on a design implemented in an off-the-shelf FPGA board in order to detect a HT and analyze its behavior. The logic testing and side-channel analysis methods are non-invasive. The run-time method is an invasive one where on-chip digital sensors are used to detect unexpected differentiations in the layout of the Integrated Circuit (IC). The side channel analysis method uses power or Electromagnetic emission signals during the cryptography process in order to perform a proposed statistical analysis approach and correlate logically the outcomes of the analysis collected with the previous methods’ results. The proposed approach does not rely on the presence of a “Golden chip” or any trusted known test values for detecting the HT. On the contrary, it proposes a statistical, heuristic, analysis using specific features, to reduce false positive HT detections, to analyze HT activations, find what triggers them and in what point in time that happens. The overall methodology is implemented, showcased and evaluated on an actual FPGA board using actual experiments and results that validate our assumptions. To the best of our knowledge this is the first attempt at combining three different parameter analysis methods for HT detection without using “trusted” measurements or chips on an off-the-shelf FPGA board.

Power analysis based reverse engineering on the secret round function of block ciphers

SUMMARYThe recent cryptanalysis on block ciphers has two major trends. Side channel analysis (SCA) has become a new threat to the hardware implementations of encryption algorithms. On the other hand, reverse engineering has been adopted to explore the unknown part of the encryption algorithms, which has become a new target of the cryptanalysis. Some drawbacks have been found in the existing methods of reverse engineering, which target on the special structures or utilize the flaws in the unknown parts. The major disadvantage is that the number of rounds to be analyzed is limited, and the complexity is high. The existing SCAs for reverse engineering depend on the leakage models in a large extent and mainly focus on the single component of the algorithms, whereas the other parts of the target algorithm are known. In this paper, we present a more general and feasible reverse analysis by combining the mathematical methods and the SCA methods. We use the strict avalanche criterion for the non‐linear operations of block ciphers and apply the power analysis to reverse the structure parameters. We propose a new reverse analysis method to reduce the dependency on the leakage models, which can be combined with the structural cryptanalysis to reverse the internal parameters of the linear and non‐linear operations. We finally achieve the reverse analysis on the unknown round function of block ciphers. Copyright © 2013 John Wiley & Sons, Ltd.