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Benefiting from cloud computing, today's early-stage quantum computers can be remotely accessed via the cloud services, known as Quantum-as-a-Service (QaaS). However, it poses a high risk of data leakage in quantum machine learning (QML). To run a QML model with QaaS, users need to locally compile their quantum circuits including the subcircuit of data encoding first and then send the compiled circuit to the QaaS provider for execution. If the QaaS provider is untrustworthy, the subcircuit to encode the raw data can be easily stolen. Therefore, we propose a co-design framework for preserving the data security of QML with the QaaS paradigm, namely PristiQ. By introducing an encryption subcircuit with extra secure qubits associated with a user-defined security key, the security of data can be greatly enhanced. And an automatic search algorithm is proposed to optimize the model to maintain its performance on the encrypted quantum data. Experimental results on simulation and the actual IBM quantum computer both prove the ability of PristiQ to provide high security for the quantum data while maintaining the model performance in QML.

Given the widespread use of safety-critical applications in the automotive field, it is crucial to ensure the Functional Safety (FuSa) of circuits and components within automotive systems. The Analog and Mixed-Signal (AMS) circuits prevalent in these systems are more vulnerable to faults induced by parametric perturbations, noise, environmental stress, and other factors, in comparison to their digital counterparts. However, their continuous signal characteristics present an opportunity for early anomaly detection, enabling the implementation of safety mechanisms to prevent system failure. To address this need, we propose a novel framework based on unsupervised machine learning for early anomaly detection in AMS circuits. The proposed approach involves injecting anomalies at various circuit locations and individual components to create a diverse and comprehensive anomaly dataset, followed by the extraction of features from the observed circuit signals. Subsequently, we employ clustering algorithms to facilitate anomaly detection. Finally, we propose a time series framework to enhance and expedite anomaly detection performance. Our approach encompasses a systematic analysis of anomaly abstraction at multiple levels pertaining to the automotive domain, from hardware- to block-level, where anomalies are injected to create diverse fault scenarios. By monitoring the system behavior under these anomalous conditions, we capture the propagation of anomalies and their effects at different abstraction levels, thereby potentially paving the way for the implementation of reliable safety mechanisms to ensure the FuSa of automotive SoCs. Our experimental findings indicate that our approach achieves 100% anomaly detection accuracy and significantly optimizes the associated latency by 5X, underscoring the effectiveness of our devised solution., Comment: 12 pages, 12 figures

Quantum computing offers significant acceleration capabilities over its classical counterpart in various application domains. Consequently, there has been substantial focus on improving quantum computing capabilities. However, to date, the security implications of these quantum computing platforms have been largely overlooked. With the emergence of cloud-based quantum computing services, it is critical to investigate the extension of classical computer security threats to the realm of quantum computing. In this study, we investigated timing-based side-channel vulnerabilities within IBM's cloud-based quantum service. The proposed attack effectively subverts the confidentiality of the executed quantum algorithm, using a more realistic threat model compared to existing approaches. Our experimental results, conducted using IBM's quantum cloud service, demonstrate that with just 10 measurements, it is possible to identify the underlying quantum computer that executed the circuit. Moreover, when evaluated using the popular Grover circuit, we showcase the ability to leak the quantum oracle with a mere 500 measurements. These findings underline the pressing need to address timing-based vulnerabilities in quantum computing platforms and advocate for enhanced security measures to safeguard sensitive quantum algorithms and data., Comment: 10 pages, 9 figures, submitted to IEEE HOST 2024

Power side-channel attacks exploit the dynamic power consumption of cryptographic operations to leak sensitive information of encryption hardware. Therefore, it is necessary to conduct power side-channel analysis for assessing the susceptibility of cryptographic systems and mitigating potential risks. Existing power side-channel analysis primarily focuses on post-silicon implementations, which are inflexible in addressing design flaws, leading to costly and time-consuming post-fabrication design re-spins. Hence, pre-silicon power side-channel analysis is required for early detection of vulnerabilities to improve design robustness. In this paper, we introduce SCAR, a novel pre-silicon power side-channel analysis framework based on Graph Neural Networks (GNN). SCAR converts register-transfer level (RTL) designs of encryption hardware into control-data flow graphs and use that to detect the design modules susceptible to side-channel leakage. Furthermore, we incorporate a deep learning-based explainer in SCAR to generate quantifiable and human-accessible explanation of our detection and localization decisions. We have also developed a fortification component as a part of SCAR that uses large-language models (LLM) to automatically generate and insert additional design code at the localized zone to shore up the side-channel leakage. When evaluated on popular encryption algorithms like AES, RSA, and PRESENT, and postquantum cryptography algorithms like Saber and CRYSTALS-Kyber, SCAR, achieves up to 94.49% localization accuracy, 100% precision, and 90.48% recall. Additionally, through explainability analysis, SCAR reduces features for GNN model training by 57% while maintaining comparable accuracy. We believe that SCAR will transform the security-critical hardware design cycle, resulting in faster design closure at a reduced design cost.

Quantum computing has proven to be capable of accelerating many algorithms by performing tasks that classical computers cannot. Currently, Noisy Intermediate Scale Quantum (NISQ) machines struggle from scalability and noise issues to render a commercial quantum computer. However, the physical and software improvements of a quantum computer can efficiently control quantum gate noise. As the complexity of quantum algorithms and implementation increases, software control of quantum circuits may lead to a more intricate design. Consequently, the verification of quantum circuits becomes crucial in ensuring the correctness of the compilation, along with other processes, including quantum error correction and assertions, that can increase the fidelity of quantum circuits. In this paper, we propose a Decision Diagram-based quantum equivalence checking approach, QuBEC, that requires less latency compared to existing techniques, while accounting for circuits with quantum error correction redundancy. Our proposed methodology reduces verification time on certain benchmark circuits by up to $271.49 \times$, while the number of Decision Diagram nodes required is reduced by up to $798.31 \times$, compared to state-of-the-art strategies. The proposed QuBEC framework can contribute to the advancement of quantum computing by enabling faster and more efficient verification of quantum circuits, paving the way for the development of larger and more complex quantum algorithms.

System-on-Chips (SoCs) form the crux of modern computing systems. SoCs enable high-level integration through the utilization of multiple Intellectual Property (IP) cores. However, the integration of multiple IP cores also presents unique challenges owing to their inherent vulnerabilities, thereby compromising the security of the entire system. Hence, it is imperative to perform hardware security validation to address these concerns. The efficiency of this validation procedure is contingent on the quality of the SoC security properties provided. However, generating security properties with traditional approaches often requires expert intervention and is limited to a few IPs, thereby resulting in a time-consuming and non-robust process. To address this issue, we, for the first time, propose a novel and automated Natural Language Processing (NLP)-based Security Property Generator (NSPG). Specifically, our approach utilizes hardware documentation in order to propose the first hardware security-specific language model, HS-BERT, for extracting security properties dedicated to hardware design. To evaluate our proposed technique, we trained the HS-BERT model using sentences from RISC-V, OpenRISC, MIPS, OpenSPARC, and OpenTitan SoC documentation. When assessedb on five untrained OpenTitan hardware IP documents, NSPG was able to extract 326 security properties from 1723 sentences. This, in turn, aided in identifying eight security bugs in the OpenTitan SoC design presented in the hardware hacking competition, Hack@DAC 2022.

Over the recent years, there has been an extensive adoption of Machine Learning (ML) in a plethora of real-world applications, ranging from computer vision to data mining and drug discovery. In this paper, we utilize ML to facilitate efficient film fabrication, specifically Atomic Layer Deposition (ALD). In order to make advances in ALD process development, which is utilized to generate thin films, and its subsequent accelerated adoption in industry, it is imperative to understand the underlying atomistic processes. Towards this end, in situ techniques for monitoring film growth, such as Spectroscopic Ellipsometry (SE), have been proposed. However, in situ SE is associated with complex hardware and, hence, is resource intensive. To address these challenges, we propose an ML-based approach to expedite film thickness estimation. The proposed approach has tremendous implications of faster data acquisition, reduced hardware complexity and easier integration of spectroscopic ellipsometry for in situ monitoring of film thickness deposition. Our experimental results involving SE of TiO2 demonstrate that the proposed ML-based approach furnishes promising thickness prediction accuracy results of 88.76% within +/-1.5 nm and 85.14% within +/-0.5 nm intervals. Furthermore, we furnish accuracy results up to 98% at lower thicknesses, which is a significant improvement over existing SE-based analysis, thereby making our solution a viable option for thickness estimation of ultrathin films.

Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many critical applications. Ensuring the reliability and robustness of the underlying AI/ML hardware becomes our paramount importance. In this paper, we explore and evaluate the reliability of different AI/ML hardware. The first section outlines the reliability issues in a commercial systolic array-based ML accelerator in the presence of faults engendering from device-level non-idealities in the DRAM. Next, we quantified the impact of circuit-level faults in the MSB and LSB logic cones of the Multiply and Accumulate (MAC) block of the AI accelerator on the AI/ML accuracy. Finally, we present two key reliability issues -- circuit aging and endurance in emerging neuromorphic hardware platforms and present our system-level approach to mitigate them., Comment: To appear at VLSI Test Symposium

Systolic array-based deep neural network (DNN) accelerators have recently gained prominence for their low computational cost. However, their high energy consumption poses a bottleneck to their deployment in energy-constrained devices. To address this problem, approximate computing can be employed at the cost of some tolerable accuracy loss. However, such small accuracy variations may increase the sensitivity of DNNs towards undesired subtle disturbances, such as permanent faults. The impact of permanent faults in accurate DNNs has been thoroughly investigated in the literature. Conversely, the impact of permanent faults in approximate DNN accelerators (AxDNNs) is yet under-explored. The impact of such faults may vary with the fault bit positions, activation functions and approximation errors in AxDNN layers. Such dynamacity poses a considerable challenge to exploring the trade-off between their energy efficiency and fault resilience in AxDNNs. Towards this, we present an extensive layer-wise and bit-wise fault resilience and energy analysis of different AxDNNs, using the state-of-the-art Evoapprox8b signed multipliers. In particular, we vary the stuck-at-0, stuck-at-1 fault-bit positions, and activation functions to study their impact using the most widely used MNIST and Fashion-MNIST datasets. Our quantitative analysis shows that the permanent faults exacerbate the accuracy loss in AxDNNs when compared to the accurate DNN accelerators. For instance, a permanent fault in AxDNNs can lead up to 66\% accuracy loss, whereas the same faulty bit can lead to only 9\% accuracy loss in an accurate DNN accelerator. Our results demonstrate that the fault resilience in AxDNNs is orthogonal to the energy efficiency., Comment: Accepted for publication in the The 22nd International Symposium on Quality Electronic Design (ISQED'21)

With transistor scaling reaching its limits, interposer-based integration of dies (chiplets) is gaining traction. Such an interposer-based integration enables finer and tighter interconnect pitch than traditional system-on-packages and offers two key benefits: 1. It reduces design-to-market time by bypassing the time-consuming process of verification and fabrication. 2. It reduces the design cost by reusing chiplets. While black-boxing of the slow design stages cuts down the design time, it raises significant security concerns. We study the security implications of the emerging interposer-based integration methodology. The black-boxed design stages deploy security measures against hardware Trojans, reverse engineering, and intellectual property piracy in traditional systems-on-chip (SoC) designs and hence are not suitable for interposer-based integration. We propose using functionally diverse chiplets to detect and thwart hardware Trojans and use the inherent logic redundancy to shore up anti-piracy measures. Our proposals do not rely on access to the black-box design stages. We evaluate the security, time and cost benefits of our plan by implementing a MIPS processor, a DCT core, and an AES core using various IPs from the Xilinx CORE GENERATOR IP catalog, on an interposer-based Xilinx FPGA.

The electric grid modernization effort relies on the extensive deployment of microgrid (MG) systems. MGs integrate renewable resources and energy storage systems, allowing to generate economic and zero-carbon footprint electricity, deliver sustainable energy to communities using local energy resources, and enhance grid resilience. MGs as cyberphysical systems include interconnected devices that measure, control, and actuate energy resources and loads. For optimal operation, cyberphysical MGs regulate the onsite energy generation through support functions enabled by smart inverters. Smart inverters, being consumer electronic firmware-based devices, are susceptible to increasing security threats. If inverters are maliciously controlled, they can significantly disrupt MG operation and electricity delivery as well as impact the grid stability. In this paper, we demonstrate the impact of denial-of-service (DoS) as well as controller and setpoint modification attacks on a simulated MG system. Furthermore, we employ custom-built hardware performance counters (HPCs) as design-for-security (DfS) primitives to detect malicious firmware modifications on MG inverters. The proposed HPCs measure periodically the order of various instruction types within the MG inverter's firmware code. Our experiments illustrate that the firmware modifications are successfully identified by our custom-built HPCs utilizing various machine learning-based classifiers.

Integrated circuits (ICs) are the foundation of all computing systems. They comprise high-value hardware intellectual property (IP) that are at risk of piracy, reverse-engineering, and modifications while making their way through the geographically-distributed IC supply chain. On the frontier of hardware security are various design-for-trust techniques that claim to protect designs from untrusted entities across the design flow. Logic locking is one technique that promises protection from the gamut of threats in IC manufacturing. In this work, we perform a critical review of logic locking techniques in the literature, and expose several shortcomings. Taking inspiration from other cybersecurity competitions, we devise a community-led benchmarking exercise to address the evaluation deficiencies. In reflecting on this process, we shed new light on deficiencies in evaluation of logic locking and reveal important future directions. The lessons learned can guide future endeavors in other areas of hardware security.

The advent of data-driven real-time applications requires the implementation of Deep Neural Networks (DNNs) on Machine Learning accelerators. Google's Tensor Processing Unit (TPU) is one such neural network accelerator that uses systolic array-based matrix multiplication hardware for computation in its crux. Manufacturing faults at any state element of the matrix multiplication unit can cause unexpected errors in these inference networks. In this paper, we propose a formal model of permanent faults and their propagation in a TPU using the Discrete-Time Markov Chain (DTMC) formalism. The proposed model is analyzed using the probabilistic model checking technique to reason about the likelihood of faulty outputs. The obtained quantitative results show that the classification accuracy is sensitive to the type of permanent faults as well as their location, bit position and the number of layers in the neural network. The conclusions from our theoretical model have been validated using experiments on a digit recognition-based DNN., Comment: 4 pages, 2 figures

In the era of Internet of Things (IoT), Malware has been proliferating exponentially over the past decade. Traditional anti-virus software are ineffective against modern complex Malware. In order to address this challenge, researchers have proposed Hardware-assisted Malware Detection (HMD) using Hardware Performance Counters (HPCs). The HPCs are used to train a set of Machine learning (ML) classifiers, which in turn, are used to distinguish benign programs from Malware. Recently, adversarial attacks have been designed by introducing perturbations in the HPC traces using an adversarial sample predictor to misclassify a program for specific HPCs. These attacks are designed with the basic assumption that the attacker is aware of the HPCs being used to detect Malware. Since modern processors consist of hundreds of HPCs, restricting to only a few of them for Malware detection aids the attacker. In this paper, we propose a Moving target defense (MTD) for this adversarial attack by designing multiple ML classifiers trained on different sets of HPCs. The MTD randomly selects a classifier; thus, confusing the attacker about the HPCs or the number of classifiers applied. We have developed an analytical model which proves that the probability of an attacker to guess the perfect HPC-classifier combination for MTD is extremely low (in the range of $10^{-1864}$ for a system with 20 HPCs). Our experimental results prove that the proposed defense is able to improve the classification accuracy of HPC traces that have been modified through an adversarial sample generator by up to 31.5%, for a near perfect (99.4%) restoration of the original accuracy., Comment: 14 pages, 17 figures

This paper reports a novel approach that uses transistor aging in an integrated circuit (IC) to detect hardware Trojans. When a transistor is aged, it results in delays along several paths of the IC. This increase in delay results in timing violations that reveal as timing errors at the output of the IC during its operation. We present experiments using aging-aware standard cell libraries to illustrate the usefulness of the technique in detecting hardware Trojans. Combining IC aging with over-clocking produces a pattern of bit errors at the IC output by the induced timing violations. We use machine learning to learn the bit error distribution at the output of a clean IC. We differentiate the divergence in the pattern of bit errors because of a Trojan in the IC from this baseline distribution. We simulate the golden IC and show robustness to IC-to-IC manufacturing variations. The approach is effective and can detect a Trojan even if we place it far off the critical paths. Results on benchmarks from the Trust-hub show a detection accuracy of $\geq$99%., Comment: 21 pages, 34 figures

Due to their growing popularity and computational cost, deep neural networks (DNNs) are being targeted for hardware acceleration. A popular architecture for DNN acceleration, adopted by the Google Tensor Processing Unit (TPU), utilizes a systolic array based matrix multiplication unit at its core. This paper deals with the design of fault-tolerant, systolic array based DNN accelerators for high defect rate technologies. To this end, we empirically show that the classification accuracy of a baseline TPU drops significantly even at extremely low fault rates (as low as $0.006\%$). We then propose two novel strategies, fault-aware pruning (FAP) and fault-aware pruning+retraining (FAP+T), that enable the TPU to operate at fault rates of up to $50\%$, with negligible drop in classification accuracy (as low as $0.1\%$) and no run-time performance overhead. The FAP+T does introduce a one-time retraining penalty per TPU chip before it is deployed, but we propose optimizations that reduce this one-time penalty to under 12 minutes. The penalty is then amortized over the entire lifetime of the TPU's operation., Comment: To appear at IEEE VLSI Test Symposium 2018

Profile-cast is a service paradigm within the communication framework of delay tolerant networks (DTN). Instead of using destination addresses to determine the final destination it uses similarity-based forwarding protocol. With the rise in popularity of various wireless networks, the need to make wireless technologies robust, resilient to attacks and failure becomes mandatory. One issue that remains to be addressed in behavioral networks is node co-operation in forwarding packets. Nodes might behave selfishly (due to bandwidth preservation, energy /power constraints) or maliciously by dropping packets or not forwarding them to other nodes based on profile similarity. In both cases the net result is degradation in the performance of the network. It is our goal to show that the performance of the behavioral network can be improved by employing self-policing scheme that would detect node misbehavior and then decide how to tackle them in order to ensure node cooperation or so that the overall performance does not fall below a certain threshold. For this various existing self-policing techniques which are in use in ad-hoc networks will be first tried on this behavioral scenario.At various stages simulation would be used to measure performances of the network under different constraints, and after subjected to different techniques