Peer to Peer (P2P) or direct connection IoT has become increasingly popular owing to its lower latency and higher privacy compared to database-driven or server-based IoT. However, wireless vulnerabilities raise severe concerns on IoT device-to-device communication. This is further aggravated by the challenge to achieve lightweight direct mutual authentication and secure key exchange between IoT peer nodes in P2P IoT applications. Physical unclonable function (PUF) is a key enabler to lightweight, low-power and secure authentication of resource-constrained devices in IoT. Nevertheless, current PUF-enabled authentication protocols, with or without the challenge-response pairs (CRPs) of each of its interlocutors stored in the verifier's side, are incompatible for P2P IoT scenarios due to the security, storage and computing power limitations of IoT devices. To solve this problem, a new lightweight PUF-based mutual authentication and key exchange protocol is proposed. It allows two resource-constrained PUF embedded endpoint devices to authenticate each other directly without the need for local storage of CRPs or any private secrets, and simultaneously establish the session key for secure data exchange without resorting to the public-key algorithm. The proposed protocol is evaluated using the game-based formal security analysis method as well as the automatic security analysis tool ProVerif to corroborate its mutual authenticity, secrecy, and resistance against replay and man-in-the-middle (MITM) attacks. Using two Avnet Ultra96-V2 boards to emulate the two IoT endpoint devices, a physical prototype system is also constructed to demonstrate and validate the feasibility of the proposed secure P2P connection scheme. A comparative analysis shows that the proposed protocol outperforms related protocols in terms of security features, computational complexity as well as communication and storage costs.

Unbalanced Cu homeostasis has been suggested to be associated with hematopoietic disease, but the roles of Cu overload in the hematopoietic system and the potential mechanisms are obscure. Here, we report a novel association and the novel potential pathways for Cu overload to induce proliferation defects in zebrafish embryonic hematopoietic stem and progenitor cells (HSPCs) via down-regulating expression of foxm1-cytoskeleton axis, which is conserved from fish to mammals. Mechanistically, we show the direct binding of Cu to transcriptional factors HSF1 and SP1 and that Cu overload induces the cytoplasmic aggregation of proteins HSF1 and SP1. These result in the reduced transcriptional activities of HSF1 and SP1 on their downstream FOXM1 as well as the FOXM1 transcriptional activities on cytoskeletons in HSPCs, which leads to ultimately cell proliferation impairment. These findings unveil the novel linkage of Cu overload with specific signaling transduction as well as the subsequent HSPC proliferation defects.

The last decade has witnessed remarkable research advances at the intersection of machine learning (ML) and hardware security. The confluence of the two technologies has created many interesting and unique opportunities, but also left some issues in their wake. ML schemes have been extensively used to enhance the security and trust of embedded systems like hardware Trojans and malware detection. On the other hand, ML-based approaches have also been adopted by adversaries to assist side-channel attacks, reverse engineer integrated circuits and break hardware security primitives like Physically Unclonable Functions (PUFs). Deep learning is a subfield of ML. It can continuously learn from a large amount of labeled data with a layered structure. Despite the impressive outcomes demonstrated by deep learning in many application scenarios, the dark side of it has not been fully exposed yet. The inability to fully understand and explain what has been done within the super-intelligence can turn an inherently benevolent system into malevolent. Recent research has revealed that the outputs of Deep Neural Networks (DNNs) can be easily corrupted by imperceptibly small input perturbations. As computations are brought nearer to the source of data creation, the attack surface of DNN has also been extended from the input data to the edge devices. Accordingly, due to the opportunities of ML-assisted security and the vulnerabilities of ML implementation, in this paper, we will survey the applications, vulnerabilities and fortification of ML from the perspective of hardware security. We will discuss the possible future research directions, and thereby, sharing a roadmap for the hardware security community in general.

The scene text interpretation is a critical part of natural scene interpretation. Currently, most of the existing work is based on high-end GPU implementation, which is commonly used on the server side. However, in IoT application scenarios, the communication overhead from edge device to the server is quite large, which sometimes even dominates the total processing time. Hence, the edge-computing oriented design is needed to solve this problem. In this paper, we present an architectural design and implementation of a natural scene text interpretation (NSTI) accelerator, which can classify and localize the text region on pixel-level efficiently in real-time on mobile devices. To target the real-time and low-latency processing, the Binary Convolutional Encoder-decoder Network (B-CEDNet) is adopted as the core architecture to enable massive parallelism due to its binary feature. Massively parallelized computations and a highly pipelined data flow control enhance its latency and throughput performance. The NSTI accelerator is implemented in a 40nm CMOS technology, which can process scene text images (size of 128x32) at 34 fps and latency of 40 ms for pixelwise interpretation with the pixelwise classification accuracy over 90% on ICDAR-03 and -13 dataset. The real energy-efficiency is 698 GOP/s/W and the peak energy-efficiency can get up to 7825 GOP/s/W. The proposed accelerator is 7× more energy efficient than its optimized GPU-based implementation counterpart, while maintaining a real-time throughput with latency of 40 ms.

Deep neural networks (DNNs) have permeated into many diverse application domains, making them attractive targets of malicious attacks. DNNs are particularly susceptible to data poisoning attacks. Such attacks can be made more venomous and harder to detect by poisoning the training samples without changing their ground-truth labels. Despite its pragmatism, the clean-label requirement imposes a stiff restriction and strong conflict in simultaneous optimization of attack stealth, success rate, and utility of the poisoned model. Attempts to circumvent the pitfalls often lead to a high injection rate, ineffective embedded backdoors, unnatural triggers, low transferability, and/or poor robustness. In this paper, we overcome these constraints by amalgamating different data augmentation techniques for the backdoor trigger. The spatial intensities of the augmentation methods are iteratively adjusted by interpolating the clean sample and its augmented version according to their tolerance to perceptual loss and augmented feature saliency to target class activation. Our proposed attack is comprehensively evaluated on different network models and datasets. Compared with state-of-the-art clean-label backdoor attacks, it has lower injection rate, stealthier poisoned samples, higher attack success rate, and greater backdoor mitigation resistance while preserving high benign accuracy. Similar attack success rates are also demonstrated on the Intel Neural Compute Stick 2 edge AI device implementation of the poisoned model after weight-pruning and quantization.

The flourishing of Internet of Things (IoT) has rekindled on-premise computing to allow data to be analyzed closer to the source. To support edge Artificial Intelligence (AI), hardware accelerators, open-source AI model compilers and commercially available toolkits have evolved to facilitate the development and deployment of applications that use AI at its core. This paradigm shift in deep learning computations does not, however, reduce the vulnerability of deep neural networks (DNN) against adversarial attacks but introduces a difficult catch-up. This is because existing methodologies rely mainly on off-line analysis to detect adversarial inputs, assuming that the deep learning model is implemented on a 32-bit floating-point graphical processing unit (GPU) instance. In this paper, we propose a new hardware-oriented approach for in-situ detection of adversarial inputs feeding through a spatial DNN accelerator architecture or a third-party DNN Intellectual Property (IP) implemented on the edge. Our method exploits controlled glitch injection into the clock signal of the DNN accelerator to maximize the information gain for the discrimination of adversarial and benign inputs. A light gradient boosting machine (lightGBM) is constructed by analyzing the prediction probability of unmutated and mutated models and the label change inconsistency between the adversarial and benign samples in the training dataset. With negligibly small hardware overhead, the glitch injection circuit and the trained lightGBM detector can be easily implemented alongside with the deep learning model on a Xilinx ZU9EG chip. The effectiveness of the proposed detector is validated against four state-of-the-art adversarial attacks on two different types and scales of DNN models, VGG16 and ResNet50, for a thousand-class visual object recognition application. The results show a significant increase in true positive rate and a substantial reduction in false positive rate on the Fast Gradient Sign Method (FGSM), Iterative-FGSM (I-FGSM), C&W and universal perturbation attacks compared with modern software-oriented adversarial sample detection methods.