DEMIX: Domain-Enforced Memory Isolation for Embedded System.

Memory isolation is an essential technology for safeguarding the resources of lightweight embedded systems. This technique isolates system resources by constraining the scope of the processor's accessible memory into distinct units known as domains. Despite the security offered by this approach, the Memory Protection Unit (MPU), the most common memory isolation method provided in most lightweight systems, incurs overheads during domain switching due to the privilege level intervention. However, as IoT environments become increasingly interconnected and more resources become required for protection, the significant overhead associated with domain switching under this constraint is expected to be crucial, making it harder to operate with more granular domains. To mitigate these issues, we propose DEMIX, which supports efficient memory isolation for multiple domains. DEMIX comprises two mainelements-Domain-Enforced Memory Isolation and instruction-level domain isolation-with the primary idea of enabling granular access control for memory by validating the domain state of the processor and the executed instructions. By achieving fine-grained validation of memory regions, our technique safely extends the supported domain capabilities of existing technologies while eliminating the overhead associated with switching between domains. Our implementation of eight user domains shows that our approach yields a hardware overhead of a slight 8% in Ibex Core, a very lightweight RISC-V processor.

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory.

Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss.

Development and Reliability of a Visual-Cognitive Reactive Triple Hop Test.

CONTEXT: Current lower-extremity return to sport testing primarily considers the physical status of an athlete; however, sport participation requires continuous cognitive dual-task engagement. Therefore, the purpose was to develop and evaluate the reliability of a visual-cognitive reactive (VCR) triple hop test that simulates the typical sport demand of combined online visual-cognitive processing and neuromuscular control to improve return to sport testing after lower-extremity injury. DESIGN: Test-retest reliability. METHODS: Twenty-one healthy college students (11 females, 23.5 [3.7] y, 1.73 [0.12] m, 73.0 [16.8] kg, Tegner Activity Scale 5.5 [1.1] points) participated. Participants performed a single-leg triple hop with and without a VCR dual task. The VCR task incorporated the FitLight system to challenge peripheral response inhibition and central working memory. Maximum hop distance, reaction time, cognitive errors, and physical errors were measured. Two identical testing visits were separated by 12 to 17 days (14 [1] d). RESULTS: Traditional triple hop (intraclass correlation coefficients: ICC(3,1) = .96 [.91-.99]; standard error of the measurement = 16.99 cm) and the VCR triple hop (intraclass correlation coefficients(3,1) = .92 [.82-.97]; standard error of the measurement = 24.10 cm) both demonstrated excellent reliability for the maximum hop distance, and moderate reliability for the VCR triple hop reaction time (intraclass correlation coefficients(3,1) = .62 [.09-.84]; standard error of the measurement = 0.09 s). On average, the VCR triple hop resulted in a hop distance deficit of 8.17% (36.4 [5.1] cm; P < .05, d = 0.55) relative to the traditional triple hop. CONCLUSIONS: Hop distance on the VCR triple hop had excellent test-retest reliability and induced a significant physical performance deficit when compared with the traditional triple hop assessment. The VCR triple hop reaction time also demonstrated moderate reliability.

Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method.

In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results.

RealPrice: Blockchain-Powered Real-Time Pricing for Software-Defined Enabled Edge Network.

With the limited Internet bandwidth in a given area, unlimited data plans can create congestion because there is no retribution for transmitting many packets. The real-time pricing mechanism can inform users of their Internet consumption to limit congestion during peak hours. However, implementing real-time pricing is opex-heavy from the network provider side and requires high-integrity operations to gain consumer trust. This paper aims to leverage the software-defined network to solve the opex issues and blockchain technology to solve trust issues. First, the network congestion level in a given area is analyzed. Then, the price is adjusted accordingly. Devices that send a lot of traffic during congestion will be charged more expensive bills than if transmitting traffic during an off-peak period. To prevent over-charging, the consumers can pre-configure a customized Internet profile stating how many data bytes they are willing to send during congestion. The software-defined controller also authenticates consumers and checks whether they have enough token deposits in the blockchain as Internet usage fees. We implement our work using Ethereum and POX controllers. The experiment results show that the proposed real-time pricing can be performed seamlessly, and the network provider can reap up to 72.91% more profits than existing approaches, such as usage-based pricing or time-dependent pricing. The fairness and trustability of real-time pricing is also guaranteed through the proof-of-usage mechanism and the transparency of the blockchain.

High-Speed and Unified ECC Processor for Generic Weierstrass Curves over GF(p) on FPGA.

In this paper, we present a high-speed, unified elliptic curve cryptography (ECC) processor for arbitrary Weierstrass curves over GF(p), which to the best of our knowledge, outperforms other similar works in terms of execution time. Our approach employs the combination of the schoolbook long and Karatsuba multiplication algorithm for the elliptic curve point multiplication (ECPM) to achieve better parallelization while retaining low complexity. In the hardware implementation, the substantial gain in speed is also contributed by our n-bit pipelined Montgomery Modular Multiplier (pMMM), which is constructed from our n-bit pipelined multiplier-accumulators that utilizes digital signal processor (DSP) primitives as digit multipliers. Additionally, we also introduce our unified, pipelined modular adder/subtractor (pMAS) for the underlying field arithmetic, and leverage a more efficient yet compact scheduling of the Montgomery ladder algorithm. The implementation for 256-bit modulus size on the 7-series FPGA: Virtex-7, Kintex-7, and XC7Z020 yields 0.139, 0.138, and 0.206 ms of execution time, respectively. Furthermore, since our pMMM module is generic for any curve in Weierstrass form, we support multi-curve parameters, resulting in a unified ECC architecture. Lastly, our method also works in constant time, making it suitable for applications requiring high speed and SCA-resistant characteristics.

CoMeT: Configurable Tagged Memory Extension.

Commodity processor architectures are releasing various instruction set extensions to support security solutions for the efficient mitigation of memory vulnerabilities. Among them, tagged memory extension (TME), such as ARM MTE and SPARC ADI, can prevent unauthorized memory access by utilizing tagged memory. However, our analysis found that TME has performance and security issues in practical use. To alleviate these, in this paper, we propose CoMeT, a new instruction set extension for tagged memory. The key idea behind CoMeT is not only to check whether the tag values in the address tag and memory tag are matched, but also to check the access permissions for each tag value. We implemented the prototype of CoMeT on the RISC-V platform. Our evaluation results confirm that CoMeT can be utilized to efficiently implement well-known security solutions, i.e., shadow stack and in-process isolation, without compromising security.

Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches.

Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world attack, are attached to the target object or its surroundings to deceive the target system. However, most previous research employed adversarial patches that are conspicuous to human vision, making them easy to identify and counter. Previously, the spatially localized perturbation GAN (SLP-GAN) was proposed, in which the perturbation was only added to the most representative area of the input images, creating a spatially localized adversarial camouflage patch that excels in terms of visual fidelity and is, therefore, difficult to detect by human vision. In this study, the use of the method called eSLP-GAN was extended to deceive classifiers and object detection systems. Specifically, the loss function was modified for greater compatibility with an object-detection model attack and to increase robustness in the real world. Furthermore, the applicability of the proposed method was tested on the CARLA simulator for a more authentic real-world attack scenario.

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification.

Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems, identification of appliances on/off events should be processed instantly. Thus, it is necessary to use an extremely short period signal of appliances to shorten the time delay for users to acquire event information. However, acquiring event information from a short period signal raises another problem. The problem is target load feature to be easily mixed with background load. The more complex the background load has, the noisier the target load occurs. This issue certainly reduces the appliance identification performance. Therefore, we provide a novel methodology that leverages Generative Adversarial Network (GAN) to generate noise distribution of background load then use it to generate a clear target load. We also built a Convolutional Neural Network (CNN) model to identify load based on single load data. Then we use that CNN model to evaluate the target load generated by GAN. The result shows that GAN is powerful to denoise background load across the complex load. It yields a high accuracy of load identification which could reach 92.04%.

A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization.

Adversarial attack techniques in deep learning have been studied extensively due to its stealthiness to human eyes and potentially dangerous consequences when applied to real-life applications. However, current attack methods in black-box settings mainly employ a large number of queries for crafting their adversarial examples, hence making them very likely to be detected and responded by the target system (e.g., artificial intelligence (AI) service provider) due to its high traffic volume. A recent proposal able to address the large query problem utilizes a gradient-free approach based on Particle Swarm Optimization (PSO) algorithm. Unfortunately, this original approach tends to have a low attack success rate, possibly due to the model's difficulty of escaping local optima. This obstacle can be overcome by employing a multi-group approach for PSO algorithm, by which the PSO particles can be redistributed, preventing them from being trapped in local optima. In this paper, we present a black-box adversarial attack which can significantly increase the success rate of PSO-based attack while maintaining a low number of query by launching the attack in a distributed manner. Attacks are executed from multiple nodes, disseminating queries among the nodes, hence reducing the possibility of being recognized by the target system while also increasing scalability. Furthermore, we utilize Multi-Group PSO with Random Redistribution (MGRR-PSO) for perturbation generation, performing better than the original approach against local optima, thus achieving a higher success rate. Additionally, we propose to efficiently remove excessive perturbation (i.e, perturbation pruning) by utilizing again the MGRR-PSO rather than a standard iterative method as used in the original approach. We perform five different experiments: comparing our attack's performance with existing algorithms, testing in high-dimensional space in ImageNet dataset, examining our hyperparameters (i.e., particle size, number of clients, search boundary), and testing on real digital attack to Google Cloud Vision. Our attack proves to obtain a 100% success rate on MNIST and CIFAR-10 datasets and able to successfully fool Google Cloud Vision as a proof of the real digital attack by maintaining a lower query and wide applicability.

Dirac Fermions in Borophene.

Honeycomb structures of group IV elements can host massless Dirac fermions with nontrivial Berry phases. Their potential for electronic applications has attracted great interest and spurred a broad search for new Dirac materials especially in monolayer structures. We present a detailed investigation of the ß_{12} sheet, which is a borophene structure that can form spontaneously on a Ag(111) surface. Our tight-binding analysis revealed that the lattice of the ß_{12} sheet could be decomposed into two triangular sublattices in a way similar to that for a honeycomb lattice, thereby hosting Dirac cones. Furthermore, each Dirac cone could be split by introducing periodic perturbations representing overlayer-substrate interactions. These unusual electronic structures were confirmed by angle-resolved photoemission spectroscopy and validated by first-principles calculations. Our results suggest monolayer boron as a new platform for realizing novel high-speed low-dissipation devices.

Thickness-dependent Dirac dispersions of few-layer topological insulators supported by metal substrate.

The surface states protected by time-reversal symmetry in 3-dimensional topological insulators have recently been confirmed by angle-resolved photoemission spectroscopy, scanning tunneling microscopy, quantum transport and so on. However, the electronic properties of ultra-thin topological insulator films have not been extensively studied, especially when the films are grown on metal substrates. In this paper, we have elucidated the local behaviors of the electronic states of ultra-thin topological insulator Bi2Se3 grown with molecular beam epitaxy on Au(111) using scanning tunneling microscopy/spectroscopy. We have observed linear dispersion of electron interference patterns at higher energies than the Fermi energy that were not accessible by conventional angle-resolved photoemission spectroscopy. Moreover, the dispersion of the interference patterns varies with the film thickness, which is explained by band bending near the interface between the topological insulator and the metal substrate. Our experiments demonstrate that interfacial effects in thin topological insulator films on metal substrate can be sensed using scanning tunneling spectroscopy.

Electrical Conductivity through a Single Atomic Step Measured with the Proximity-Induced Superconducting Pair Correlation.

Local disordered nanostructures in an atomically thick metallic layer on a semiconducting substrate play significant and decisive roles in transport properties of two-dimensional (2D) conductive systems. We measured the electrical conductivity through a step of monoatomic height in a truly microscopic manner by using as a signal the superconducting pair correlation induced by the proximity effect. The transport property across a step of a one-monolayer Pb surface metallic phase, formed on a Si(111) substrate, was evaluated by inducing the pair correlation around the local defect and measuring its response, i.e., the reduced density of states at the Fermi energy using scanning tunneling microscopy. We found that the step resistance has a significant contribution to the total resistance on a nominally flat surface. Our study also revealed that steps in the 2D metallic layer terminate the propagation of the pair correlation. Superconductivity is enhanced between the first surface step and the superconductor-normal-metal interface by reflectionless tunneling when the step is located within a coherence length.

Site-Dependent Evolution of Electrical Conductance from Tunneling to Atomic Point Contact.

Using scanning tunneling microscopy (STM), we investigated the evolution of electrical conductance between a Pb tip and Pb(111) surface from tunneling to atomic point contact at a site that was defined with atomic precision. We found that the conductance evolution depended on the contact site, for instance, on-top, bridge, or hollow (hcp and fcc) sites in the Pb lattice. In the transition from tunneling to contact regimes, the conductance measured at the on-top site was enhanced. In the point contact regime, the hollow sites had conductances larger than those of the other sites, and between the hollow sites, the hcp site had a conductance larger than that of the fcc site. We also observed the enhancement and reversal of the apparent height in atomically resolved high-current STM images, consistent with the results of the conductance traces. Our results indicate the importance of atomic configuration in the conductance of atomic junctions and suggest that attractive chemical interactions have a significant role in electron transport between contacting atoms.

Imaging Josephson vortices on the surface superconductor Si(111)-(√7×√3)-In using a scanning tunneling microscope.

We have studied the superconducting Si(111)-(√7×√3)-In surface using a ³He-based low-temperature scanning tunneling microscope. Zero-bias conductance images taken over a large surface area reveal that vortices are trapped at atomic steps after magnetic fields are applied. The crossover behavior from Pearl to Josephson vortices is clearly identified from their elongated shapes along the steps and significant recovery of superconductivity within the cores. Our numerical calculations combined with experiments clarify that these characteristic features are determined by the relative strength of the interterrace Josephson coupling at the atomic step.

Efficient hardware implementation of the lightweight block encryption algorithm LEA.

Recently, due to the advent of resource-constrained trends, such as smartphones and smart devices, the computing environment is changing. Because our daily life is deeply intertwined with ubiquitous networks, the importance of security is growing. A lightweight encryption algorithm is essential for secure communication between these kinds of resource-constrained devices, and many researchers have been investigating this field. Recently, a lightweight block cipher called LEA was proposed. LEA was originally targeted for efficient implementation on microprocessors, as it is fast when implemented in software and furthermore, it has a small memory footprint. To reflect on recent technology, all required calculations utilize 32-bit wide operations. In addition, the algorithm is comprised of not complex S-Box-like structures but simple Addition, Rotation, and XOR operations. To the best of our knowledge, this paper is the first report on a comprehensive hardware implementation of LEA. We present various hardware structures and their implementation results according to key sizes. Even though LEA was originally targeted at software efficiency, it also shows high efficiency when implemented as hardware.

Small private key MQPKS on an embedded microprocessor.

Multivariate quadratic (MQ) cryptography requires the use of long public and private keys to ensure a sufficient security level, but this is not favorable to embedded systems, which have limited system resources. Recently, various approaches to MQ cryptography using reduced public keys have been studied. As a result of this, at CHES2011 (Cryptographic Hardware and Embedded Systems, 2011), a small public key MQ scheme, was proposed, and its feasible implementation on an embedded microprocessor was reported at CHES2012. However, the implementation of a small private key MQ scheme was not reported. For efficient implementation, random number generators can contribute to reduce the key size, but the cost of using a random number generator is much more complex than computing MQ on modern microprocessors. Therefore, no feasible results have been reported on embedded microprocessors. In this paper, we propose a feasible implementation on embedded microprocessors for a small private key MQ scheme using a pseudo-random number generator and hash function based on a block-cipher exploiting a hardware Advanced Encryption Standard (AES) accelerator. To speed up the performance, we apply various implementation methods, including parallel computation, on-the-fly computation, optimized logarithm representation, vinegar monomials and assembly programming. The proposed method reduces the private key size by about 99.9% and boosts signature generation and verification by 5.78% and 12.19% than previous results in CHES2012.

Effects of G and SH Truncation on the Replication, Virulence, and Immunogenicity of Avian Metapneumovirus.

Four mutants varying the length of the G and SH genes, including a G-truncated mutant (ΔG) and three G/SH-truncated mutants (ΔSH/G-1, ΔSH/G-2, and ΔSH/G-3), were generated via serially passaging the avian metapneumovirus strain SNU21004 into the cell lines Vero E6 and DF-1 and into embryonated chicken eggs. The mutant ΔG particles resembled parental virus particles except for the variance in the density of their surface projections. G and G/SH truncation significantly affected the viral replication in chickens' tracheal ring culture and in infected chickens but not in the Vero E6 cells. In experimentally infected chickens, mutant ΔG resulted in the restriction of viral replication and the attenuation of the virulence. The mutants ΔG and ΔSH/G-1 upregulated three interleukins (IL-6, IL-12, and IL-18) and three interferons (IFNα, IFNß, and IFNγ) in infected chickens. In addition, the expression levels of innate immunity-related genes such as Mda5, Rig-I, and Lgp2, in BALB/c mice were also upregulated when compared to the parental virus. Immunologically, the mutant ΔG induced a strong, delayed humoral immune response, while the mutant ΔSH/G-1 induced no humoral immune response. Our findings indicate the potential of the mutant ΔG but not the mutant ΔSH/G-1 as a live attenuated vaccine candidate.

Supramolecular Cl⋠⋠⋠H and O⋠⋠⋠H interactions in self-assembled 1,5-dichloroanthraquinone layers on Au(111).

The role of halogen bonds in self-assembled networks for systems with Br and I ligands has recently been studied with scanning tunneling microscopy (STM), which provides physical insight at the atomic scale. Here, we study the supramolecular interactions of 1,5-dichloroanthraquinone molecules on Au(111), including Cl ligands, by using STM. Two different molecular structures of chevron and square networks are observed, and their molecular models are proposed. Both molecular structures are stabilized by intermolecular Cl⋅⋅⋅H and O⋅⋅⋅H hydrogen bonds with marginal contributions from Cl-related halogen bonds, as revealed by density functional theory calculations. Our study shows that, in contrast to Br- and I-related halogen bonds, Cl-related halogen bonds weakly contribute to the molecular structure due to a modest positive potential (σ hole) of the Cl ligands.

Recovery and local-variation of Dirac cones in oxygen-intercalated graphene on Ru(0001) studied using scanning tunneling microscopy and spectroscopy.

Methods to decouple epitaxial graphene from metal substrates have been extensively studied, with anticipation of observing unperturbed Dirac cone properties, but its local electronic structures were rarely studied. Here, we investigated the local variations of Dirac cones recovered using oxygen intercalation applied to epitaxial graphene on Ru(0001) using scanning tunneling microscopy and spectroscopy (STM and STS). New V-shaped features, which appear in the STS data at the oxygen-intercalated graphene regions, are attributed to the signatures of recovered Dirac cones. The Dirac point energy was observed at 0.48 eV below the Fermi level, different from previous photoemission results because of different oxygen coverages. The observed spatial variations of Dirac point energy were explained by the weakly protruding network structures caused by a small net strain in graphene. Our study shows that oxygen-intercalated graphene provides an excellent platform for further graphene research at the nano-meter scale with unperturbed Dirac cones.