Cyber Physical Systems (CPS) have increasingly started using Learning Enabled Components (LECs) for performing perception-based control tasks. The simple design approach, and their capability to continuously learn has led to their widespread use in different autonomous applications. Despite their simplicity and impressive capabilities, these models are difficult to assure, which makes their use challenging. The problem of assuring CPS with untrusted controllers has been achieved using the Simplex Architecture. This architecture integrates the system to be assured with a safe controller and provides a decision logic to switch between the decisions of these controllers. However, the key challenges in using the Simplex Architecture are: (1) designing an effective decision logic, and (2) sudden transitions between controller decisions lead to inconsistent system performance. To address these research challenges, we make three key contributions: (1) \textit{dynamic-weighted simplex strategy} -- we introduce ``weighted simplex strategy" as the weighted ensemble extension of the classical Simplex Architecture. We then provide a reinforcement learning based mechanism to find dynamic ensemble weights, (2) \textit{middleware framework} -- we design a framework that allows the use of the dynamic-weighted simplex strategy, and provides a resource manager to monitor the computational resources, and (3) \textit{hardware testbed} -- we design a remote-controlled car testbed called DeepNNCar to test and demonstrate the aforementioned key concepts. Using the hardware, we show that the dynamic-weighted simplex strategy has 60\% fewer out-of-track occurrences (soft constraint violations), while demonstrating higher optimized speed (performance) of 0.4 m/s during indoor driving than the original LEC driven system.
Vanderbilt University（范德堡大学）；Technical University of Munich（慕尼黑工业大学）
Learning Enabled Components (LEC) have greatly assisted cyber-physical systems in achieving higher levels of autonomy. However, LEC's susceptibility to dynamic and uncertain operating conditions is a critical challenge for the safety of these systems. Redundant controller architectures have been widely adopted for safety assurance in such contexts. These architectures augment LEC "performant" controllers that are difficult to verify with "safety" controllers and the decision logic to switch between them. While these architectures ensure safety, we point out two limitations. First, they are tra…
University of Waterloo（滑铁卢大学）；Chongqing University（重庆大学）
As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years. This paper focuses on discussing the decision-making (DM) strategy for autonomous vehicles in a connected environment. First, the highway DM problem is formulated, wherein the vehicles can exchange information via wireless networking. Then, two classical reinforcement learning (RL) algorithms, Q-learning and Dyna, are leveraged to derive the DM strategies in a predefined driving scenario. Finally, the control performance of the derived DM policies in safety and ef…
University of South Florida（南佛罗里达大学）；United States Military Academy, West Point（西点军校）
Cyber vulnerability management is a critical function of a cybersecurity operations center (CSOC) that helps protect organizations against cyber-attacks on their computer and network systems. Adversaries hold an asymmetric advantage over the CSOC, as the number of deficiencies in these systems is increasing at a significantly higher rate compared to the expansion rate of the security teams to mitigate them in a resource-constrained environment. The current approaches are deterministic and one-time decision-making methods, which do not consider future uncertainties when prioritizing and selecti…
Policy-Guided Threat Hunting: An LLM enabled Framework with Splunk SOC Triage
Department of Management Information Systems, University of Illinois, Springfield, USA（美国伊利诺伊大学斯普林菲尔德分校管理信息系统系）；School of Computing and Communications, Lancaster University, United Kingdom（英国兰卡斯特大学计算与通信学院）；School of Computer Applications, Kalinga Institute of Industrial Technology, India（印度卡林加工业技术学院计算机应用学院）；School of Information Technology and Artificial Intelligence, University of Cumberlands, USA（美国坎伯兰大学信息技术与人工智能学院）
Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach
Macquarie University（麦考瑞大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Yale University（耶鲁大学）；University of Wisconsin Madison（威斯康星大学麦迪逊分校）；University of Southern California（南加州大学）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；University of Pennsylvania（宾夕法尼亚大学）
DURA-CPS: A Multi-Role Orchestrator for Dependability Assurance in LLM-Enabled Cyber-Physical Systems
Cyrion Labs（Cyrion实验室）；Carnegie Mellon University（卡内基梅隆大学）；Cornell University（康奈尔大学）；University of California San Diego（加州大学圣地亚哥分校）；University of Montana（蒙大拿大学）；Oakland University（奥克兰大学）；University of Pennsylvania（宾夕法尼亚大学）
AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space
Shenzhen Institute of Information Technology（深圳信息科技研究院）；University of Science and Technology Beijing（北京科技大学）；Dublin City University（都柏林城市大学）；Blekinge Institute of Technology（布莱金厄学院）
Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization
Department of Mathematical and Computer Sciences, University of Sarajevo（萨拉热窝大学数学与计算机科学系）；Singidunum University（辛吉杜姆大学）；Trinity University（特里尼蒂大学）；Sinergija University（辛格里雅大学）
From Prompt to Physical Actuation: Holistic Threat Modeling of LLM-Enabled Robotic Systems
School of Informatics, Computing, and Cyber Systems, Northern Arizona University（信息学、计算与网络系统学院，北亚利桑那大学）；Department of Software Science, Tallinn University of Technology（软件科学系，塔林技术大学）
Development of Cyber Physical Systems (CPSs) requires close interaction between developers with expertise in many domains to achieve ever-increasing demands for improved performance, reduced cost, and more system autonomy. Each engineering discipline commonly relies on domain-specific modeling languages, and analysis and execution of these models is often automated with appropriate tooling. However, integration between these heterogeneous models and tools is often lacking, and most of the burden for inter-operation of these tools is placed on system developers. To address this problem, we intr…
Learning-enabled controllers used in cyber-physical systems (CPS) are known to be susceptible to adversarial attacks. Such attacks manifest as perturbations to the states generated by the controller's environment in response to its actions. We consider state perturbations that encompass a wide variety of adversarial attacks and describe an attack scheme for discovering adversarial states. To be useful, these attacks need to be natural, yielding states in which the controller can be reasonably expected to generate a meaningful response. We consider shield-based defenses as a means to improve co…
Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. However, reusing components with AI capabilities might introduce new risks as there is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We know from SW-intensive systems that AI algorithms are generally fragile and behave unexpectedly to changes in context and boundary conditions. The question we address in this paper is, what ty…
