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In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of activation functions quantum circuits for integration into fault-tolerant quantum computing architectures, with an emphasis on minimizing T-depth. Specifically, we present novel implementations of ReLU and leaky ReLU activation functions, achieving constant T-depths of 4 and 8, respectively. Leveraging quantum lookup tables, we extend our exploration to other activation functions such as the sigmoid. This approach enables us to customize precision and T-depth by adjusting the number of qubits, making our results more adaptable to various application scenarios. This study represents a significant advancement towards enhancing the practicality and application of quantum machine learning.

Abstract In recent years, numerous research advancements have extended the limit of classical simulation of quantum algorithms. Although, most of the state‐of‐the‐art classical simulators are only limited to binary quantum systems, which restrict the classical simulation of higher‐dimensional quantum computing systems. Through recent developments in higher‐dimensional quantum computing systems, it is realised that implementing qudits improves the overall performance of a quantum algorithm by increasing memory space and reducing the asymptotic complexity of a quantum circuit. Hence, in this article, QuDiet, a state‐of‐the‐art user‐friendly python‐based higher‐dimensional quantum computing simulator is introduced. QuDiet offers multi‐valued logic operations by utilising generalised quantum gates with an abstraction so that any naive user can simulate qudit systems with ease as compared to the existing ones. Various benchmark quantum circuits is simulated in QuDiet and show the considerable speedup in simulation time as compared to the other simulators without loss in precision. Finally, QuDiet provides a full qubit‐qudit hybrid quantum simulator package with quantum circuit templates of well‐known quantum algorithms for fast prototyping and simulation. Comprehensive simulation up to 20 qutrits circuit on depth 80 on QuDiet was successfully achieved. The complete code and packages of QuDiet is available at https://github.com/LegacYFTw/QuDiet.

Abstract In this paper, we propose an efficient quantum carry-lookahead adder based on the higher radix structure. For the addition of two n-bit numbers, our adder uses $$O(n)-O(\frac{n}{r})$$ O ( n ) - O ( n r ) qubits and $$O(n)+O(\frac{n}{r})$$ O ( n ) + O ( n r ) T gates to get the correct answer in T-depth $$O(r)+O(\log {\frac{n}{r}})$$ O ( r ) + O ( log n r ) , where r is the radix. Quantum carry-lookahead adder has already attracted some attention because of its low T-depth. Our work further reduces the overall cost by introducing a higher radix layer. By analyzing the performance in T-depth, T-count, and qubit count, it is shown that the proposed adder is superior to existing quantum carry-lookahead adders. Even compared to the Draper out-of-place adder which is very compact and efficient, our adder is still better in terms of T-count.

Abstract The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. RRAM has grown in prominence in the recent years due to its simple structure, long retention, high operating speed, ultra-low-power operation capabilities, ability to scale to lower dimensions without affecting the device performance and the possibility of three-dimensional integration for high-density applications. Over the past few years, research has shown RRAM as one of the most suitable candidates for designing efficient, intelligent and secure computing system in the post-CMOS era. In this manuscript, the journey and the device engineering of RRAM with a special focus on the resistive switching mechanism are detailed. This review also focuses on the RRAM based on two-dimensional (2D) materials, as 2D materials offer unique electrical, chemical, mechanical and physical properties owing to their ultrathin, flexible and multilayer structure. Finally, the applications of RRAM in the field of neuromorphic computing are presented.

In this work, we propose generic and novel adaptations to the binary Plaintext-Checking (PC) oracle based side-channel attacks for Kyber KEM. These attacks operate in a chosen-ciphertext setting, and are fairly generic and easy to mount on a given target, as the attacker requires very minimal information about the target device. However, these attacks have an inherent disadvantage of requiring a few thousand traces to perform full key recovery. This is due to the fact that these attacks typically work by recovering a single bit of information about the secret key per query/trace. In this respect, we propose novel parallel PC oracle based side-channel attacks, which are capable of recovering a generic P number of bits of information about the secret key in a single query/trace. We propose novel techniques to build chosen-ciphertexts so as to efficiently realize a parallel PC oracle for Kyber KEM. We also build a multi-class classifier, which is capable of realizing a practical side-channel based parallel PC oracle with very high success rate. We experimentally validated the proposed attacks (upto P = 10) on the fastest implementation of unprotected Kyber KEM in the pqm4 library. Our experiments yielded improvements in the range of 2.89× and 7.65× in the number of queries, compared to state-of-the-art binary PC oracle attacks, while arbitrarily higher improvements are possible for a motivated attacker, given the generic nature of the proposed attacks. We further conduct a thorough study on applicability to different scenarios, based on the presence/absence of a clone device, and also partial key recovery. Finally, we also show that the proposed attacks are able to achieve the lowest number of queries for key recovery, even for implementations protected with low-cost countermeasures such as shuffling. Our work therefore, concretely demonstrates the power of PC oracle attacks on Kyber KEM, thereby stressing the need for concrete countermeasures such as masking for Kyber and other lattice-based KEMs.

In this work, we present the first fault injection analysis of the Number Theoretic Transform (NTT). The NTT is an integral computation unit, widely used for polynomial multiplication in several structured lattice-based key encapsulation mechanisms (KEMs) and digital signature schemes. We identify a critical single fault vulnerability in the NTT, which severely reduces the entropy of its output. This in turn enables us to perform a wide-range of attacks applicable to lattice-based KEMs as well as signature schemes. In particular, we demonstrate novel key recovery and message recovery attacks targeting the key generation and encryption procedure of Kyber KEM. We also propose novel existential forgery attacks targeting deterministic and probabilistic signing procedure of Dilithium, followed by a novel verification bypass attack targeting its verification procedure. All proposed exploits are demonstrated with high success rate using electromagnetic fault injection on optimized implementations of Kyber and Dilithium, from the open-source pqm4 library on the ARM Cortex-M4 microcontroller. We also demonstrate that our proposed attacks are capable of bypassing concrete countermeasures against existing fault attacks on lattice-based KEMs and signature schemes. We believe our work motivates the need for more research towards development of countermeasures for the NTT against fault injection attacks.

Abstract Deep learning has evolved as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. The advancement of deep learning has been so radical that today it can surpass human‐level performance. As a consequence, deep learning is being extensively used in most of the recent day‐to‐day applications. However, efficient deep learning systems can be jeopardised by using crafted adversarial samples, which may be imperceptible to the human eye, but can lead the model to misclassify the output. In recent times, different types of adversaries based on their threat model leverage these vulnerabilities to compromise a deep learning system where adversaries have high incentives. Hence, it is extremely important to provide robustness to deep learning algorithms against these adversaries. However, there are only a few strong countermeasures which can be used in all types of attack scenarios to design a robust deep learning system. Herein, the authors attempt to provide a detailed discussion on different types of adversarial attacks with various threat models and also elaborate on the efficiency and challenges of recent countermeasures against them.

Abstract As the fault‐based attacks are becoming a more pertinent threat in today's era of edge computing/internet‐of‐things, there is a need to streamline the existing tools for better accuracy and ease of use, so that we can gauge the attacker's power and a proper countermeasure can be devised in the long run. In this regard, we propose a machine learning (ML) assisted tool that can be used in the context of a differential fault attack. In particular, finding the exact fault location by analysing the output difference (typically the XOR of the nonfaulty and the faulty ciphertexts) is somewhat nontrivial. During the literature survey, we notice that the Pearson's correlation coefficient dominantly is used for this purpose, and has almost become the defacto standard. While this method can yield good accuracy for certain cases, we argue that an ML‐based method is more powerful in all the situations we experiment with. We substantiate our claim by showing the relative performances (we choose the commonly used multilayer perceptron as our ML tool) with two variants of Grain‐128a (a stream cipher, and a stream cipher with authentication), the lightweight stream cipher LIZARD and the lightweight block cipher SIMON‐32 (where the faults are injected at the fifth last rounds). Our results demonstrate that a common ML tool can outperform the correlation with the same training/testing data. We believe that our work extends the state‐of‐the‐art by showing how traditional cryptographic methods can be replaced by a more powerful ML tool.

Abstract Background Hi-C and its variant techniques have been developed to capture the spatial organization of chromatin. Normalization of Hi-C contact map is essential for accurate modeling and interpretation of high-throughput chromatin conformation capture (3C) experiments. Hi-C correction tools were originally developed to normalize systematic biases of karyotypically normal cell lines. However, a vast majority of available Hi-C datasets are derived from cancer cell lines that carry multi-level DNA copy number variations (CNVs). CNV regions display over- or under-representation of interaction frequencies compared to CN-neutral regions. Therefore, it is necessary to remove CNV-driven bias from chromatin interaction data of cancer cell lines to generate a euploid-equivalent contact map. Results We developed the HiCNAtra framework to compute high-resolution CNV profiles from Hi-C or 3C-seq data of cancer cell lines and to correct chromatin contact maps from systematic biases including CNV-associated bias. First, we introduce a novel ‘entire-fragment’ counting method for better estimation of the read depth (RD) signal from Hi-C reads that recapitulates the whole-genome sequencing (WGS)-derived coverage signal. Second, HiCNAtra employs a multimodal-based hierarchical CNV calling approach, which outperformed OneD and HiNT tools, to accurately identify CNVs of cancer cell lines. Third, incorporating CNV information with other systematic biases, HiCNAtra simultaneously estimates the contribution of each bias and explicitly corrects the interaction matrix using Poisson regression. HiCNAtra normalization abolishes CNV-induced artifacts from the contact map generating a heatmap with homogeneous signal. When benchmarked against OneD, CAIC, and ICE methods using MCF7 cancer cell line, HiCNAtra-corrected heatmap achieves the least 1D signal variation without deforming the inherent chromatin interaction signal. Additionally, HiCNAtra-corrected contact frequencies have minimum correlations with each of the systematic bias sources compared to OneD’s explicit method. Visual inspection of CNV profiles and contact maps of cancer cell lines reveals that HiCNAtra is the most robust Hi-C correction tool for ameliorating CNV-induced bias. Conclusions HiCNAtra is a Hi-C-based computational tool that provides an analytical and visualization framework for DNA copy number profiling and chromatin contact map correction of karyotypically abnormal cell lines. HiCNAtra is an open-source software implemented in MATLAB and is available at https://github.com/AISKhalil/HiCNAtra .

Abstract Background Detection of DNA copy number alterations (CNAs) is critical to understand genetic diversity, genome evolution and pathological conditions such as cancer. Cancer genomes are plagued with widespread multi-level structural aberrations of chromosomes that pose challenges to discover CNAs of different length scales, and distinct biological origins and functions. Although several computational tools are available to identify CNAs using read depth (RD) signal, they fail to distinguish between large-scale and focal alterations due to inaccurate modeling of the RD signal of cancer genomes. Additionally, RD signal is affected by overdispersion-driven biases at low coverage, which significantly inflate false detection of CNA regions. Results We have developed CNAtra framework to hierarchically discover and classify ‘large-scale’ and ‘focal’ copy number gain/loss from a single whole-genome sequencing (WGS) sample. CNAtra first utilizes a multimodal-based distribution to estimate the copy number (CN) reference from the complex RD profile of the cancer genome. We implemented Savitzky-Golay smoothing filter and Modified Varri segmentation to capture the change points of the RD signal. We then developed a CN state-driven merging algorithm to identify the large segments with distinct copy numbers. Next, we identified focal alterations in each large segment using coverage-based thresholding to mitigate the adverse effects of signal variations. Using cancer cell lines and patient datasets, we confirmed CNAtra’s ability to detect and distinguish the segmental aneuploidies and focal alterations. We used realistic simulated data for benchmarking the performance of CNAtra against other single-sample detection tools, where we artificially introduced CNAs in the original cancer profiles. We found that CNAtra is superior in terms of precision, recall and f-measure. CNAtra shows the highest sensitivity of 93 and 97% for detecting large-scale and focal alterations respectively. Visual inspection of CNAs revealed that CNAtra is the most robust detection tool for low-coverage cancer data. Conclusions CNAtra is a single-sample CNA detection tool that provides an analytical and visualization framework for CNA profiling without relying on any reference control. It can detect chromosome-level segmental aneuploidies and high-confidence focal alterations, even from low-coverage data. CNAtra is an open-source software implemented in MATLAB®. It is freely available at https://github.com/AISKhalil/CNAtra.

In this work, we propose generic and novel side-channel assisted chosenciphertext attacks on NTRU-based key encapsulation mechanisms (KEMs). These KEMs are IND-CCA secure, that is, they are secure in the chosen-ciphertext model. Our attacks involve the construction of malformed ciphertexts. When decapsulated by the target device, these ciphertexts ensure that a targeted intermediate variable becomes very closely related to the secret key. An attacker, who can obtain information about the secret-dependent variable through side-channels, can subsequently recover the full secret key. We propose several novel CCAs which can be carried through by using side-channel leakage from the decapsulation procedure. The attacks instantiate three different types of oracles, namely a plaintext-checking oracle, a decryptionfailure oracle, and a full-decryption oracle, and are applicable to two NTRU-based schemes, which are NTRU and NTRU Prime. The two schemes are candidates in the ongoing NIST standardization process for post-quantum cryptography. We perform experimental validation of the attacks on optimized and unprotected implementations of NTRU-based schemes, taken from the open-source pqm4 library, using the EM-based side-channel on the 32-bit ARM Cortex-M4 microcontroller. All of our proposed attacks are capable of recovering the full secret key in only a few thousand chosen ciphertext queries on all parameter sets of NTRU and NTRU Prime. Our attacks, therefore, stress on the need for concrete side-channel protection strategies for NTRUbased KEMs.

Background: The revolution in next-generation sequencing (NGS) technology has allowed easy access and sharing of high-throughput sequencing datasets of cancer cell lines and their integrative analyses. However, long-term passaging and culture conditions introduce high levels of genomic and phenotypic diversity in established cell lines resulting in strain differences. Thus, clonal variation in cultured cell lines with respect to the reference standard is a major barrier in systems biology data analyses. Therefore, there is a pressing need for a fast and entry-level assessment of clonal variations within cell lines using their high-throughput sequencing data. Results: We developed a Python-based software, AStra, for de novo estimation of the genome-wide segmental aneuploidy to measure and visually interpret strain-level similarities or differences of cancer cell lines from whole-genome sequencing (WGS). We demonstrated that aneuploidy spectrum can capture the genetic variations in 27 strains of MCF7 breast cancer cell line collected from different laboratories. Performance evaluation of AStra using several cancer sequencing datasets revealed that cancer cell lines exhibit distinct aneuploidy spectra which reflect their previously-reported karyotypic observations. Similarly, AStra successfully identified large-scale DNA copy number variations (CNVs) artificially introduced in simulated WGS datasets. Conclusions: AStra provides an analytical and visualization platform for rapid and easy comparison between different strains or between cell lines based on their aneuploidy spectra solely using the raw BAM files representing mapped reads. We recommend AStra for rapid first-pass quality assessment of cancer cell lines before integrating scientific datasets that employ deep sequencing. AStra is an open-source software and is available at https://github.com/AISKhalil/AStra .

In this work, we demonstrate generic and practical EM side-channel assisted chosen ciphertext attacks over multiple LWE/LWR-based Public Key Encryption (PKE) and Key Encapsulation Mechanisms (KEM) secure in the chosen ciphertext model (IND-CCA security). We show that the EM side-channel information can be efficiently utilized to instantiate a plaintext checking oracle, which provides binary information about the output of decryption, typically concealed within IND-CCA secure PKE/KEMs, thereby enabling our attacks. Firstly, we identified EM-based side-channel vulnerabilities in the error correcting codes (ECC) enabling us to distinguish based on the value/validity of decrypted codewords. We also identified similar vulnerabilities in the Fujisaki-Okamoto transform which leaks information about decrypted messages applicable to schemes that do not use ECC. We subsequently exploit these vulnerabilities to demonstrate practical attacks applicable to six CCA-secure lattice-based PKE/KEMs competing in the second round of the NIST standardization process. We perform experimental validation of our attacks on implementations taken from the open-source pqm4 library, running on the ARM Cortex-M4 microcontroller. Our attacks lead to complete key-recovery in a matter of minutes on all the targeted schemes, thus showing the effectiveness of our attack.

Unsere zunehmende Abhängigkeit von Informationstechnik erhöht kontinuierlich die Safety- und Security-Anforderungen bei deren Einsatz. Ein zentrales Problem hierbei sind Schwachstellen von Hard- und Software. Marktkräfte konnten diese Situation bislang nicht grundsätzlich beheben. Eine Gegenstrategie sollte deshalb folgende Optionen erwägen: (1) private und staatliche Förderung offener und sicherer IT‑Produktion, (2) Verbesserung der souveränen Kontrolle bei der Produktion aller kritischen IT‑Komponenten innerhalb eines Wirtschaftsraumes sowie (3) verbesserte und durchgesetzte Regulierung. Dieser Beitrag analysiert Vor- und Nachteile dieser Optionen. Es wird vorgeschlagen, die Sicherheit der Schlüsselkomponenten einer Lieferkette durch weltweit verteilte, offene und ggf. mathematisch bewiesene Komponenten zu gewährleisten. Der beschriebene Ansatz erlaubt die Nutzung existierender und neuer proprietärer Komponenten.

Emerging Non-Volatile Memories (NVMs) such as Magnetic RAM (MRAM), Spin-Transfer Torque RAM (STTRAM), Phase Change Memory (PCM) and Resistive RAM (RRAM) are very promising due to their low (static) power operation, high scalability and high performance. However, these memories bring new threats to data security. In this paper, we investigate their vulnerability against Side Channel Attack (SCA). We assume that the adversary can monitor the supply current of the memory array consumed during read/write operations and recover the secret key of Advanced Encryption Standard (AES) execution. First, we show our analysis of simulation results. Then, we use commercial NVM chips to validate the analysis. We also investigate the effectiveness of encoding against SCA on emerging NVMs. Finally, we summarize two new flavors of NVMs that can be resilient against SCA. To the best of our knowledge, this is the first attempt to do a comprehensive study of SCA vulnerability of the majority of emerging NVM-based cache.

Due to the fast changing wireless communication standards coupled with strict performance constraints, the demand for flexible yet high-performance architectures is increasing. To tackle the flexibility requirement, software-defined radio (SDR) is emerging as an obvious solution, where the underlying hardware implementation is tuned via software layers to the varied standards depending on power-performance and quality requirements leading to adaptable, cognitive radio. In this paper, we conduct a case study for representatives of two complexity classes of WCDMA channel estimation algorithms and explore the effect of flexibility on energy efficiency using different implementation options. Furthermore, we propose new design guidelines for both highly specialized architectures and highly flexible architectures using high-level synthesis, to enable the required performance and flexibility to support multiple applications. Our experiments with various design points show that the resulting architectures meet the performance constraints of WCDMA and a wide range of options are offered for tuning such architectures depending on power/performance/area constraints of SDR.