Side Channel Leakage Alignment Based on Longest Common Subsequence

Researchers are exploring the use of convolutional neural networks (CNNs) in side-channel attacks to understand the weaknesses in cryptographic implementation. CNNs can learn hierarchical characteristics automatically from electromagnetic radiation or power usage during cryptographic processes. Researchers train CNNs on side-channel data to extract meaningful representations and deduce secret keys. Deep learning algorithms are helpful in evaluating the security of embedded systems, and CNNs are a feasible paradigm for profiling side-channel analysis attacks. In this paper, it has been introduced a VGG (Visual Geometry Group)-Net architecture, which is a typical deep convolutional neural network design with numerous layers. It uses the ASCAD dataset to conduct experiments. They found that VGG-Net architecture Side Channel Attacks (SCA) provides better results than the previously optimized CNN model by significantly reducing the number of side-channel traces required for successful attacks on desynchronized datasets. The researchers also discovered that synchronous traces serve as the pre-training source for VGG-Net architecture, functioning successfully in terms of jittering with minimal fine-adjusting after training

Modern network-on-chip (NoC) hardware is an emerging target for side-channel security attacks. A recent work implemented and characterized timing-based software side-channel attacks that target NoC hardware on a real multicore machine. This article studies the impact of system noise on prior attack setups and shows that high noise is sufficient to defeat the attacker. We propose an information theory-based attack setup that uses repetition codes and differential signaling techniques to de-noise the unwanted noise from the NoC channel to successfully implement a practical covert-communication attack on a real multicore machine. The evaluation demonstrates an attack efficacy of 97%, 88%, and 78% under low, medium, and high external noise, respectively. Our attack characterization reveals that noise-based mitigation schemes are inadequate to prevent practical covert communication, and thus isolation-based mitigation schemes must be considered to ensure strong security. Isolation-based schemes are shown to mitigate timing-based side-channel attacks. However, their impact on the performance of real-world security critical workloads is not well understood in the literature. This article evaluates the performance implications of state-of-the-art spatial and temporal isolation schemes. The performance impact is shown to range from 2–3% for a set of graph and machine learning workloads, thus making isolation-based mitigations practical.

Side Channel Attack (SCA) recovers secret information from an embedded device with implementation of cryptographic algorithm by exploiting its physical leakages. For most SCA methods to achieve good performance, the measured leakages are often desired to be well aligned. However, due to some specific reasons such as inaccurate measurements or carefully designed countermeasures, misalignment of leakages frequently occurs in practice. Misalignment significantly reduces the efficiency of SCA methods, or even makes them fail. To address this issue, two alignment approaches are proposed: a local alignment based on shotgun distance and a global alignment based on weighted edit distance . Compared with previous methods, the proposed methods are capable of keeping the secret dependant leakages, while not introducing any redundant information. In addition, the proposed methods could also reduce the negative effects of noise, which is another factor seriously decreasing the efficiency of SCA methods. Interestingly, it is pretty easy to set appropriate parameters for these two methods. Practical experiments show that the proposed methods outperform previous methods in three different circumstances and different noise levels.

In this paper the effect of high-performance techniques for high speed applications in secure cryptographic implementations is studied. The use of dual pre charge logic styles with fine-grained pipelining with an overlapping three-phase clock scheme is studied, also including a correct distribution of the clock signal in the cryptographic implementation. To make this study, four different implementations of the Sbox-9 of the Kasumi algorithm have been implemented using an 90nm TSMC technology. Simulation-based DPA attacks have been carried out, showing how the proper synchronization of data signals gives better results in terms of power consumption and operating frequency, but affects negatively the security against side channel attacks, decreasing the number of input patterns needed to disclosure the secret key.

In side channel attack (SCA) studies, it is widely believed that unprotected implementations leak information about the intermediate states of the internal cryptographic process. However, directly recovering the intermediate states is not common practice in today’s SCA study. Instead, most SCAs exploit the leakages in a “guess-and-determine” way, where they take a partial key guess, compute the corresponding intermediate states, then try to identify which one fits the observed leakages better. In this paper, we ask whether it is possible to take the other way around—directly learning the intermediate states from the side channel leakages. Under certain circumstances, we find that the intermediate states can be efficiently recovered with the well-studied Independent Component Analysis (ICA). Specifically, we propose several methods to convert the side channel leakages into effective ICA observations. For more robust recovery, we also present a specialized ICA algorithm which exploits the specific features of circuit signals. Experiments confirm the validity of our analysis in various circumstances, where most intermediate states can be correctly recovered with only a few hundred traces. Our approach brings new possibilities to the current SCA study, including building an alternative SCA distinguisher, directly attacking the middle encryption rounds and reverse engineering with fewer restrictions. Considering its potential in more advanced applications, we believe our ICA-based SCA deserves more research attention in the future study.

Embedded systems are increasingly providing secure functionalities, which often rely on some dedicated hardware for symmetric and public-key cryptography. When resources are limited, elliptic curve cryptography (ECC) may be chosen instead of the more widely known RSA, which needs much longer keys for the same security level. However, ECC may be vulnerable, as any other cryptographic implementation, to side channel analysis, which may reveal secret information by analyzing collateral sources of information, such as power consumption. Countermeasures must be thus adopted at the design level, in order to ensure robust and secure operation of the device. We propose here a new scalar multiplication algorithm on an elliptic curve, based on a novel randomized window method. This design is protected against side channel attacks (Timing, Simple and Differential Power Analysis) and it is implemented over prime fields, but it can be applied to binary fields as well. In order to evaluate this countermeasure, we provide its costs, and an estimation of the additional entropy added to the computation against side channels attacks.

Side-channel attacks are a prominent threat to the security of cryptographic implementations. Differently from the traditional black-box attacks, which exploit the inputs and outputs of cryptographic schemes, side-channel attacks partially access the inner working of the scheme as well, by observing the physical leakage emitted by the device executing cryptographic algorithms. A notable example is the class of power-analysis attacks, which exploits the power consumption of the underlying device to recover the secret keys of the implemented cryptosystem. Since their first presentation in the late 1990s, the problem of securing cryptographic systems in the presence of side-channel leakages received significant attention by the cryptographic community. In particular, the theory community has concentrated its research efforts on formally modeling the side-channel leakages and on designing cryptographic schemes \textit{provably} secure in such leakage models. However, conceiving a formal model that realistically captures possible physical leakages and constructing primitives sufficiently efficient to be deployed in practice are still challenging research objectives. This research area is usually referred to as leakage-resilient cryptography. In this dissertation, we examine three main research directions. The first one focuses on the technique of masking, which is the most popular approach to counteract power-analysis attacks. In this context, our work analyzes possible methods to improve the efficiency of masked implementations, with the goal of overcoming the decrease in performance, which is a common drawback of masking. In particular, we provide new and more efficient algorithms for the computation of basic operations in two kinds of masking schemes: the Boolean masking and the Inner Product masking. The second research direction of this work addresses the problem of formalizing security requirements for hardware implementations. While provable security of software-oriented masking has been studied since the origin of leakage resilient cryptography, the more complex task of formally proving the security of hardware-oriented masking is receiving attention by the community only recently. We discuss the different challenges of this field, and we introduce a new theoretical model, the robust probing model, capturing the conditions required for securely implementing complex algorithms in practice, providing a guide to practical implementers. Finally, the last part of this work is concerned with the protection of algorithms employed in lattice-based constructions, which are studied in the field of post-quantum cryptography. While several works investigate the application of masking schemes to standard cryptosystems, for most post-quantum schemes this is still an ongoing research. We contribute to this research area, by applying the masking countermeasure to a binomial sampler which is at the base of many lattice-based cryptosystems.

Course 4 Random matrices, the ulam problem, directed polymers & growth models, and sequence matching

Spiking neural networks (SNNs) are quickly gaining traction as a viable alternative to deep neural networks (DNNs). Compared to DNNs, SNNs are computationally more powerful and energy efficient. The design metrics (synaptic weights, membrane threshold, etc.) chosen for such SNN architectures are often proprietary and constitute confidential intellectual property (IP). Our study indicates that SNN architectures implemented using conventional analog neurons are susceptible to side channel attack (SCA). Unlike the conventional SCAs that are aimed to leak private keys from cryptographic implementations, SCANN (SCA̲ of spiking n̲eural n̲etworks) can reveal the sensitive IP implemented within the SNN through the power side channel. We demonstrate eight unique SCANN attacks by taking a common analog neuron (axon hillock neuron) as the test case. We chose this particular model since it is biologically plausible and is hence a good fit for SNNs. Simulation results indicate that different synaptic weights, neurons/layer, neuron membrane thresholds, and neuron capacitor sizes (which are the building blocks of SNN) yield distinct power and spike timing signatures, making them vulnerable to SCA. We show that an adversary can use templates (using foundry-calibrated simulations or fabricating known design parameters in test chips) and analysis to identify the specifications of the implemented SNN.

Sparse LCS Common Substring Alignment

Practical algorithms for transposition-invariant string-matching

In this paper, we address the problem of recognizing human actions from videos. Human actions recognition is a challenging task in computer vision. We propose a method to solve this problem using Longest Common Sub-Sequence (LCSS) algorithm and Shape Context (SC). Our contributions in this paper are twofold. First, we show the applicability of the SC as a pairwise shape-similarity measurement for generating a sequence that defines a specific motion. Secondly, we demonstrate the usability of LCSS to classify human actions. Experiments were performed on two action datasets to compare the result to the related methods.

Most discriminating segment – Longest common subsequence (MDSLCS) algorithm for dynamic hand gesture classification

Together with masking, shuffling is one of the most frequently considered solutions to improve the security of small embedded devices against side-channel attacks. In this paper, we provide a comprehensive study of this countermeasure, including improved implementations and a careful information theoretic and security analysis of its different variants. Our analyses lead to important conclusions as they moderate the strong security improvements claimed in previous works. They suggest that simplified versions of shuffling (e.g. using random start indexes) can be significantly weaker than their counterpart using full permutations. We further show with an experimental case study that such simplified versions can be as easy to attack as unprotected implementations. We finally exhibit the existence of “indirect leakages” in shuffled implementations that can be exploited due to the different leakage models of the different resources used in cryptographic implementations. This suggests the design of fully shuffled (and efficient) implementations, were both the execution order of the instructions and the physical resources used are randomized, as an interesting scope for further research.

Finding Longest Common Subsequence (LCS) involves comparison of two or more sequences and find the Longest Subsequence which is common to all sequences. This NP hard problem is useful in various Sequence Database (SDB) applications such as finding Motif, gene/protein sequence comparisons in Biological SDB, finding the highly demanded item list from the Inventory SDB, finding the most traded Shares list in Stock Trading SDB. The existing algorithms have drawbacks of high computational time as they focused on both matched and unmatched positions of sequences to get LCS. This is overcome by proposed Positional_LCS by focusing only on matched positions. Thus the Positional_LCS reduces the time complexity than the existing algorithms.