Biblio

Model checking is one of the most commonly used technique in formal verification. However, the exponential scale state space renders exhaustive state enumeration inefficient even for a moderate System on Chip (SoC) design. In this paper, we propose a method that leverages symbolic execution to accelerate state space search and pinpoint security vulnerabilities. We automatically convert the hardware design to functionally equivalent C++ code and utilize the KLEE symbolic execution engine to perform state exploration through heuristic search. To reduce the search space, we symbolically represent essential input signals while making non-critical inputs concrete. Experiment results have demonstrated that our method can precisely identify security vulnerabilities at significantly lower computation cost.

The Enterprise Architecture and Systems Engineering communities are often faced with complexity barriers that develop due to the fact that modern systems must be agile and resilient. This requires dynamic changes to the system so as to adapt to changing missions as well as changes in the internal and external environments. The requirement is not entirely new, but practitioners need guidance on how to manage the life cycle of such systems. This is a problem because we must be able to architect systems by alleviating the difficulties in systems life cycle management (e.g., by helping the enterprise- or systems engineer organise and maintain models and architecture descriptions of the system of interest). Building on Pask’s conversation theoretic model of aware (human or machine) individuals, the paper proposes a reference model for systems that maintain their own models real time, act efficiently, and create system-level awareness on all levers of aggregation.

Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS's phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS's random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS's phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.

The advancement of AI enables the evolution of machines from relatively simple automation to completely autonomous systems that augment human capabilities with improved quality and productivity in work and life. The singularity is near! However, humans are still vulnerable. The COVID-19 pandemic reminds us of our limited knowledge about nature. The recent accidents involving Boeing 737 Max passengers ring the alarm again about the potential risks when using human-autonomy symbiosis technologies. A key challenge of safe and effective human-autonomy teaming is enabling “trust” between the human-machine team. It is even more challenging when we are facing insufficient data, incomplete information, indeterministic conditions, and inexhaustive solutions for uncertain actions. This calls for the imperative needs of appropriate design guidance and scientific methodologies for developing safety-critical autonomous systems and AI functions. The question is how to build and maintain a safe, effective, and trusted partnership between humans and autonomous systems. This talk discusses a context-based and interaction-centred design (ICD) approach for developing a safe and collaborative partnership between humans and technology by optimizing the interaction between human intelligence and AI. An associated trust model IMPACTS (Intention, Measurability, Performance, Adaptivity, Communications, Transparency, and Security) will also be introduced to enable the practitioners to foster an assured and calibrated trust relationship between humans and their partner autonomous systems. A real-world example of human-autonomy teaming in a military context will be explained to illustrate the utility and effectiveness of these trust enablers.

In the existing scheduling algorithms of mimicry structure, the random algorithm cannot solve the problem of large vulnerability window in the process of random scheduling. Based on known vulnerabilities, the algorithm with diversity and complexity as scheduling indicators can not only fail to meet the characteristic requirements of mimic's endogenous security for defense, but also cannot analyze the unknown vulnerabilities and measure the continuous differences in time of mimic Executive Entity. In this paper, from the Angle of attack surface is put forward based on mimicry attack the mimic Executive Entity scheduling algorithm, its resources to measure analysis method and mimic security has intrinsic consistency, avoids the random algorithm to vulnerability and modeling using known vulnerabilities targeted, on time at the same time can ensure the diversity of the Executive body, to mimic the attack surface web server scheduling system in continuous time is less, and able to form a continuous differences. Experiments show that the minimum symbiotic resource scheduling algorithm based on time continuity is more secure than the random scheduling algorithm.