MUX-based logic locking (LL) is a hardware security technique that inserts multiplexers (MUX) into circuits to secure them against unauthorized use and reverse engineering by protecting original circuit pathways. Nevertheless, MUX-based LL is vulnerable to Oracle-Guided (OG) and Oracle-Less (OL) attacks. While OG methods, such as the SAT attack, are infeasible for large-scale designs, OL attacks, like those based on machine learning (ML), can exploit structural leakage in locked circuits to recover original pathways. This study introduces N-MUX, an innovative MUX-based LL approach designed to resist state-of-the-art (SOTA) ML attacks. N-MUX effectively reduces structural leakage by identifying maximal overlap structures in the original circuit to configure the MUX logic. Additionally, N-MUX ensures high efficiency by selecting the false input from the direct neighbourhood of the true input. Experimental results on ISCAS’85 and ITC’99 benchmarks demonstrate that N-MUX is the most secure and reliable LL technique against SOTA ML-based attacks, achieving an 81% reduction in attack accuracy compared to existing MUX-based LL methods and delivering up to 480× greater efficiency.

In this paper, we propose a novel pre-processing technique known as the Connectivity Matrix (CM). Building upon the foundation of the CM, we present an effective Second-Order Side-Channel Attack, called Connectivity Matrix Attack (CMA). Our work aims to efficiently counter hardware devices fortified with Masking countermeasures, and it contributes in three significant ways. First, the proposed CM has lower data complexity, as it is constant regarding the number of measurements. Second, we propose the decomposition of the CMs and the utilization of their eigenvalues as feature vectors in CMA. This approach effectively removes noisy components from the CMs and reduces their dimensions. Third, the proposed CMA employs the selected eigenvalues to establish a frequency distribution, followed by a chi-square test. This approach allows CMA to expose both the linear and non-linear leakages present in CMs. The proposed CMA is validated on the public dataset ASCAD and can reveal all the masked bytes successfully. Notably, the concept of the Connectivity Matrix extends beyond the confines of a correlation matrix used in this paper, opening the door to a promising avenue for future research.

We present a wide-range in situ random voltage dithering (WIS-RVD) on async-logic advanced encryption standard (AES) engine to counteract side-channel attacks (SCAs). There are three contributions in this brief. First, we propose the WIS-RVD based on a dual-rail asynchronous-logic (async-logic) AES engine, leveraging on the self-timed clockless operations for robust encryption under dynamic voltage and timing variations. Second, we propose an in situ voltage dithering to dither the supply voltage instantaneously during the encryption, without the requirement of additional control circuits for clock modulation, to increase the SCA resistance. Third, we propose a wide-range voltage swing technique that spans from 0.3 V (subthreshold) to 1.1 V (above threshold), obfuscating the transistor’s current models between subthreshold and threshold voltage to further enhance SCA resistance. We perform comprehensive SCA evaluations with 50-M power and EM measurements, and the SCA evaluations show that our proposed WIS-RVD on async-logic AES accelerator can resist SCAs with 50-M measurements, i.e.,$\gt 2083\times $and$\gt 2778\times $improvement for power and EM SCAs, respectively, when compared to the standard synchronous-logic AES.

Leaf area index(LAI), defined as half of total leaf area per unit ground surface area, is a critical structural parameter. The objective of this paper is to validate MODIS LAI product(MOD15A2H) using decametric-scale sentinel-2 LAI and field measured LAI. The research was conducted in Yucheng station and the main crop types in Yucheng site are winter wheat and summer maize. After atmospheric correction and Snap software process, we obtain S2(Sentinel-2) 10m resolution LAI. The S2 10m LAI was validated by comparing with field measured LAI. The S2 10m LAI showed good agreement with field measured LAI(R2= 0.81, RMSE = 0.6). To convert the S2 10m LAI to the same 500m spacial resolution with MOD15A2H, we proposed a MODIS-Like upscaling method, the Sentinel-2 500m LAI was obtained. The MOD15A2H showed very good agreement with Sentinel-2 500m LAI(R2= 0.96, RMSE = 0.23). However, the comparison result of field measured LAI and MOD15A2H(R2= 0.68, RMSE = 0.58) was poor. Finally, we analyzed the time series of field measured LAI, sentinel-2 10m LAI, sentinel-2 500m LAI and MOD15A2H LAI.

We present a side-channel attack (SCA) resistant asynchronous-logic (async-logic) AES accelerator that integrates synchronous-logic (sync-logic) Block RAMs (BRAMs) in FPGA as the Substitution-Box. We successfully identify the timing requirements to integrate sync-logic BRAMs in our async-logic AES accelerator and validate our proposed AES accelerator on the Sakura-X FPGA board. With the integration of BRAMs, we improve the resource utilization on FPGA by$1.6\times$when compared to the state-of-the-art async-logic AES accelerator, while reducing the power overhead by$1.4\times$. We comprehensively evaluate the SCA resistance of our proposed async-logic AES accelerator with 11 attacking models in both time and frequency domains. Based on our evaluations, we show that our proposed async-logic AES accelerator is highly secure against SCA with 30 million EM traces. This is more than$6000\times$improvement when compared to the benchmark sync-logic AES accelerator and$1.5\times$improvement when compared to the state-of-the-art async-logic AES accelerator.

Clipping distortion is a common problem when the amplitude of input signals exceeds the desired region of an analog-to-digital converter. Unlimited Sensing Framework (USF) alleviates the clipping distortion by folding out-of-range signals into a within-range via modulo operations. The USF signal recovery assumes an infinitesimal residual step time in the modulo operation which is generally practically infeasible. Its recovery error is inevitable due to the remainder sampling error during the residual step transition time. Instead of the infinitesimal assumption, a residual-remainder coupled USF is proposed to eliminate the remainder sampling error by coupling the residual sampling component. It is shown that the proposed coupling framework does not only relax the oversampling rate in the original USF to approaching the Nyquist sampling rate, but also provides a more accurate signal recovery capability as the remainder sampling error is eliminated by the coupling of the residual components.

Differential Deep Learning Analysis (DDLA) is a deep learning-based non-profiling side-channel attack leveraging neural networks to classify Physical Leakage Information with labels. To avoid the Class Imbalance Problem (CIP) of significantly different data sizes in different data groups, DDLA employs bit labels. However, applying bit labels will be less effective for exploiting leakage. In this paper, we propose to employ Correlation optimization Deep Learning Analysis (CO-DLA) to circumvent the CIP in DDLA by converting the classification in DDLA into a correlation optimization. Bus labels can then be used to exploit stronger leakage information. To validate the attack efficacy improvement, we perform experiments on ASCAD synchronized and de-synchronized masked AES-128 datasets. For the synchronized masked dataset, our proposed CO-DLA requires only 5k traces, which is 75% lesser than the 20k traces required by the reported DDLA, to reveal the key-byte. For the 2 de-synchronized masked datasets, our proposed CO-DLA requires only 10k traces to reveal the key-byte from both of them while the reported DDLA fails to reveal the key-byte.

We present a side-channel-attack (SCA) resistant asynchronous-logic (async-logic) Advanced Encryption Standard (AES) accelerator embodying both the masking and hiding SCA countermeasures. Our async-logic masked AES accelerator adopts a dual-rail data encoding to perform the masked 128-bit AES operations, and to enable dual-hiding to moderate both the amplitude (vertical dimension) and the time (horizontal dimension) of the side-channel signals. We implement our async-logic masked AES accelerator in FPGA and comprehensively perform the SCA evaluations based on the electromagnetic (EM) emanation. The SCA evaluations are performed based on bus-wise Hamming Distance model, bus-wise & bit-wise Hamming Weight models, and Zero-Value (ZV) model. Based on our experiment results, we show that our async-logic masked AES is secured against SCA with 1 million EM emanations. This is at least $8.3 \times$ more resistant than synchronous-logic masked AES and $200 \times$ more resistant than the synchronous-logic unmasked AES.

Encryption in field-programmable gate array (FPGA) often provides a good security solution to protect data privacy in Internet-of-Things systems, but this security solution can be compromised by side-channel attacks (SCAs). In this article, we present an FPGA-based dual-hiding asynchronous-logic (async-logic) advanced encryption standard (AES) accelerator, which is highly resistant against SCAs and yet low area/energy overheads. The proposed AES accelerator achieves vertical (amplitude) SCA hiding via an area-efficient dual-rail mapping approach and a zero-value (ZV) compensated substitution-box (S-Box), while enhancing the horizontal (temporal) SCA hiding of async-logic operations via a timing-boundary-free input arrival-time randomizer and a skewed-delay controller. A comprehensive SCA evaluation is performed with 11 SCA models, and we show that our proposed design can offer a strong SCA resistance with measurement-to-disclosure (MTD) of >20 million traces. To our best knowledge, our design is the most secure AES design evaluated with the largest number of traces in FPGA. To compare the design overheads for security, we quantify the figure of merit as normalized (Area$\times $Energy/MTD(All)$\times 10^{6}$). The figure of merit of our proposed design is$403\times $smaller than the benchmark dual-rail synchronous-logic design and$95\times $smaller than a reported async-logic design.

The attack efficacy of Differential Power Analysis (DPA), a popular side channel evaluation technique for key extraction, is compromised by the false highest Difference Of Means (DOMs) value ('ghost peaks') in the DOMs matrix produced in a conventional DPA. The ghost peak is generated by the wrong key guess and always occurs in the conventional DPA when the number of side channel traces is not enough. In this paper, an improved version of the conventional DPA termed as Normalized DPA (NDPA) is proposed to circumvent the ghost peak. With the analysis on the generation of ghost peaks in the conventional DPA, we observed that by normalizing the DOMs matrix, the ghost peaks can be greatly suppressed. We model the proposed NDPA mathematically and show that it performs better than the conventional DPA. We further provide the experimental validations on a set of 200k power simulation traces on AES S- Box and 500 EM traces from ASCAD dataset. Based on the attack results of these datasets, our proposed NDPA requires (up to 68%) lesser number of traces to reveal a correct key when compared to the conventional DPA.

Summary Epilepsy seriously damages the physical and mental health of patients. Detection of epileptic EEG signals in different periods can help doctors diagnose the disease. The change of frequency components during epilepsy seizures is obvious, and there may be noises in epilepsy EEG signals. Moreover, epileptic seizures are closely related to the release of neuronal spiking in the brain. In this paper, we propose an approach for epilepsy period classification based on combination feature extraction methods and spiking swarm intelligent optimization classification algorithm. First, combination feature extraction methods take in account both the time‐frequency features and principal component features of epilepsy. The time‐frequency features are obtained by WPT or STFT‐PSD, and noises are removed while extracting principal component features by PCA. Second, spiking swarm intelligent optimization classification algorithm takes advantage of individual cooperation and information interaction with strong robustness. Its simulated neurons are closer to reality, which consider more information and obtain stronger computing power. The experimental results show that the average classification accuracy of the proposed method can reach 98.95% and the highest classification accuracy can reach 100%. Compared with other methods, the proposed method has the best classification performance.

In this paper, we propose two normalization techniques to reduce the ghost peaks occurring in Differential Power Analysis (DPA). Ghost peaks can be defined as the DPA output generated by the wrong key guesses, having higher amplitudes than the DPA output generated by the correct key guess. We further propose variance-based Differential Power Analysis (vDPA) to attack masked crypto devices. The proposed normalization techniques and vDPA constitute four contributions. First, based on the side-channel signal modeling with the linear coefficient representing the strength of the linear component in a side-channel signal, we formulate the condition function of linear coefficients for the appearance of ghost peaks in DPA. Second, we propose pre-normalization in DPA and mathematically analyze how it can reduce ghost peaks by modulating the strength of the linear components in side-channel signals. Third, we propose post-normalization and mathematically analyze how it can reduce ghost peaks by de-correlating the strength of the linear components in side-channel signals with the condition function for the appearance of ghost peaks. Fourth, we propose vDPA to apply simultaneously with either one of the proposed normalization techniques to effectively attack masked crypto devices. Based on the experiments, we show that the proposed basic vDPA (without normalization), pre-normalized vDPA and post-normalized vDPA are all able to reveal the secret key from ASCAD data set. The pre- and post-normalized vDPAs require up to 18× and 14× fewer traces than the basic vDPA respectively. While attacking ASCAD data set, the proposed pre- and post-normalized vDPAs are both 13, 095× faster than the reported 2nd order CPA, and reveal the key-bytes successfully with only half of side-channel traces required by the reported Zero-offset DPA.

We propose a 5-port asynchronous-logic Network-on-Chip (ANoC) router based on the Sense-Amplifier Half-Buffer (SAHB) approach for cryptographic processing cores to counteract side channel attack differential power analysis (DPA) in multicore platform. There are three features in the proposed DPA-resistant ANoC router. First, the proposed ANoC router embodies dual-supply-voltage SAHB cells, where the non-critical subsidiary supply voltage is adjustable from 0.3V to 1.2V, increasing the noise variance and hence reducing the Signal-to-Noise (SNR) ratio to hide the information leakage. Second, the proposed ANoC router performs as a noise engine by increasing the number of power-on IO ports, further randomizing the overall power dissipation. Third, the proposed ANoC router can switch between DPA-resistant mode and energy-efficient nominal (non-secure) mode, saving the power dissipation when the DPA secure countermeasure is unnecessary. Based on 65nm CMOS process, the multicore platform embedded with the proposed ANoC router is implemented, and the experiment is demonstrated by running the advanced encryption standard (AES) cryptography operation. When benchmarked against the nominal mode, the noise power variance of the proposed ANoC router increases by 2.3× in the DPA-resistant mode, reducing the overall SNR ratio by 56%. When comparing to other reported noise engines, our proposed ANoC router is one of the most DPA-secure, area-efficient and power-efficient designs for multicore cryptographic applications.

We evaluate the vulnerability of a pipelined Advanced Encryption Standard (AES) against Correlation Power Analysis (CPA) Side-Channel Attack (SCA). We identify that the registers in pipelined AES are most vulnerable against CPA SCA and propose a new power model targeting the switching activities of the registers. The proposed power model is constructed based on the Hamming Distance (HD) between the intermediate values stored in the registers in two consecutive clock cycles. Then, we analyze the vulnerability of pipelined AES under two scenarios. First, during regular pipeline operation where the device is performing AES pipeline operation. Second, in non-pipeline operation where we assume the adversaries can insert delay to the input of the device to increase the signal to noise ratio of the physical leakage information. The simulation results show that under regular pipelined operation, our proposed power model can reveal all the 16 key bytes in less than 4,900 traces, resulting in 4.7× more effective than the conventional power models. Under non-pipelined operation, our proposed power model requires only 590 traces to reveal all the 16 key bytes, which is 5.9× more effective than other power models.

With the developing of smart sensors and mobile devices produces an increasing volume of data, and it captures the states of transportation infrastructures. Such data are collected and uploaded frequently, which forms the heavy data calculation and storage. Moreover, state of monitored object may be keeping the same or slight change according to a certain state during a period, such as moving vehicles on a certain path with a basic uniform speed. Therefore, if trajectory pattern of vehicles can be obtained through the state change mode, scale and update frequency of the data can be greatly reduced. Based on the above-mentioned ideas, we are aware that the trajectory data storage is divided into traceability storage and vector storage, where original sampled data from sensing device, and state vectors are extracted from the analysis of the original sample data. In this way, only a relatively small amount of vector data is stored. The system will not only effectively reduce the frequency of sampling data storage, but also reduce query and analysis operations involved with the amount of data. The vector function is used to represent road network with indexes, and the data query based on the road network is used to extract the semantic information. Our experimental results show that our proposed methodologies have significant improvements in intelligent transportation.

Due to the diversity of body movements and uncertainty of recording occasion, human action recognition is still a challenging task, especially in real world. This paper provides a new method of representing the video with mid-level vision representation which is extracted from the discriminative supervoxels. In the proposed method, the discriminative supervoxels we extracted through a learning phase frequently occur within class and are distinguishing enough between classes. They contain the meaningful parts of the video, including specific background of an action and the moving human body. The video is first oversegmented to obtain supervoxels, which are described by the dense trajectories and Bag-Of-Words framework. Afterwards, the discriminative supervoxels are extracted by an iterative procedure through training and selecting. Finally the videos are represented with discriminative supervoxels. Experimental results on KTH, YouTube and UT-Interaction datasets demonstrate comparable performance with state-of-the-art models.