As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.

Side channels are present in all systems, including digital ones. This article analyzes electromagnetic side channels from both the attack and defense perspectives.

The ubiquitous presence of printed circuit boards (PCBs) in modern electronic systems and embedded devices makes their integrity a top security concern. To take advantage of the economies of scale, today’s PCB design and manufacturing are often performed by suppliers around the globe, exposing them to many security vulnerabilities along the segmented PCB supply chain. Moreover, the increasing complexity of the PCB designs also leaves ample room for numerous sneaky board-level attacks to be implemented throughout each stage of a PCB’s lifetime, threatening many electronic devices. In this paper, we propose PDNPulse , a power delivery network (PDN) based PCB anomaly detection framework that can identify a wide spectrum of board-level malicious modifications. PDNPulse leverages the fact that the PDN’s characteristics are inevitably affected by modifications to the PCB. By detecting changes to the PDN impedance profile against the golden model and using the Frechet distance-based anomaly detection algorithms, PDNPulse can robustly and successfully discern malicious modifications across the system. Using PDNPulse, we conduct extensive experiments on seven commercial-off-the-shelf PCBs, covering different design scales, different threat models, and seven different anomaly types. The results confirm that PDNPulse creates an effective security asymmetry between attack and defense.

Over the years, autonomous systems have entered almost all the facets of human life. Gradually, higher levels of autonomy are being incorporated into cyber-physical systems (CPS) and Internet-of-things (IoT) devices. However, safety and security has always been a lurking fear behind adoption of autonomous systems such as self-driving vehicles. To address these issues, we develop a framework for quantifying trust in autonomous system. This framework consist of an estimation method, which considers effect of adversarial attacks on sensor measurements. Our estimation algorithm uses a set-membership method during identification of safe states of the system. An important feature of this algorithm is that it can distinguish between adversarial noise and other disturbances. We also verify the autonomous system by first modeling it as networks of priced timed automata (NPTA) with stochastic semantics and then using statistical probabilistic model checking to verify it against probabilistic specifications. The verification process ensures that the autonomous system behave in accordance to safety specifications within a probabilistic threshold. For quantifying trust on the system, we use confidence results provided by the model checking tool. We have demonstrated our approach by using a case study of adaptive cruise control system under sensor spoofing attacks.

Electromagnetic (EM) side-channel analysis is a powerful attack for extracting secret information from cryptographic hardware implementations. Countermeasures have been proposed at the register-transfer level (RTL), layout level, and device level. However, existing EM radiation modeling and side-channel vulnerability mitigation methods do not consider the structural resilience of original designs, nor do they provide fine-grained security enhancements to those vulnerable submodules/components. These universal solutions may introduce unnecessary overheads on the circuit under protection and may not be optimized for individual designs. In this article, we propose a design/synthesis for side-channel security evaluation and optimization framework based on the ${t}$ -test evaluation results derived from RTL hardware implementations. While the framework apply to different side-channel leakage, we focus more on EM side channels. Supported by this framework, different RTL implementations of the same cryptographic algorithm will be evaluated for their side-channel resistance. In vulnerable implementations, submodules with the most significant side-channel leakages will be identified. Security design/synthesis rules will then be applied to these vulnerable submodules for security enhancements against side-channel attacks (SCAs). Experiments, including simulations and FPGA implementations on different AES designs, are performed to validate the effectiveness of the proposed framework as well as the security design/synthesis rules.

Large Language Models (LLMs) have precipitated emerging trends towards intelligent automation. However, integrating LLMs into the hardware debug domain encounters challenges: the datasets for LLMs for hardware are often plagued by a dual dilemma – scarcity and subpar quality. Traditional hardware debug approaches that rely on experienced labor to generate detailed prompts are not cheaply scalable. Similarly, strategies that depend on existing LLMs and randomly generated prompts fail to achieve sufficient reliability. We propose a directed, semi-synthetic data synthetic method that leverages version control information and journalistic event descriptions. To produce high-quality data, this approach utilizes version control data from hardware projects combined with the 5W1H (Who, What, When, Where, Why, How) journalistic principles. It facilitates the linear scaling of dataset volumes without depending on skilled labor. We have implemented this method on a collected dataset of open-source hardware designs and fine-tuned fifteen general-purpose LLMs to enable their capability in hardware debugging tasks, thereby validating the efficacy of our approach.