In this article, we consider how to increase trust in development processes in which there is a risk for adversarial manipulation, and the adversary's objectives and resources are either ill-specified, imprecisely specified, or unknown. In such problems, we must hedge against the risk of misapprehension of attacker objectives and resources, which is further complicated in the absence of adversarial training data. We show how to model dynamic agent interaction, on the basis of partially observed or noise-corrupted data, using a partially observable Markov game (POMG) framework. We then propose a threefold heuristic solution procedure that: 1) uses the POMG to generate potential adversarial policies; 2) explicitly incorporates these adversarial policies in the construction of a robust defender policy by solving a robust dynamic program (DP); and 3) employs a probability matching heuristic in partially observable environments.

Trust in a microelectronics-based system can be characterized as the level of confidence that a system is free of subversive alterations made during system development, or that the development process of a system has not been manipulated by a malicious adversary. Trust in systems has become an increasing concern over the past decade. This article presents a novel game-theoretic framework, called GPLADD (Graph-based Probabilistic Learning Attacker and Dynamic Defender), for analyzing and quantifying system trustworthiness at the end of the development process, through the analysis of risk of development-time system manipulation. GPLADD represents attacks and attacker-defender contests over time. It treats time as an explicit constraint and allows incorporating the informational asymmetries between the attacker and defender into analysis. GPLADD includes an explicit representation of attack steps via multi-step attack graphs, attacker and defender strategies, and player actions at different times. GPLADD allows quantifying the attack success probability over time and the attacker and defender costs based on their capabilities and strategies. This ability to quantify different attacks provides an input for evaluation of trust in the development process. We demonstrate GPLADD on an example attack and its variants. We develop a method for representing success probability for arbitrary attacks and derive an explicit analytic characterization of success probability for a specific attack. We present a numeric Monte Carlo study of a small set of attacks, quantify attack success probabilities, attacker and defender costs, and illustrate the options the defender has for limiting the attack success and improving trust in the development process.

Functional flow block diagrams (FFBDs) are a traditional tool of systems engineering and remain popular in some systems engineering domains. However, their lack of formal definition makes FFBDs imprecise and impossible to rigorously analyze. The inability to analyze FFBDs may allow specification errors to remain undetected until well into the system design process or, worse, until the system is operational. To help address these problems, we have developed a precise formal syntax and semantics for FFBDs, based on the application of metamodels and the process algebra Communicating Sequential Processes (CSP). FFBDs constructed within our formalized framework are precisely defined and amenable to analyses of properties, such as safety, progress, and conformance to required scenarios. We demonstrate some of the analyses made possible by our formalization in a simple case study of system specification and show how our formalization can be used to detect and correct subtle system errors during the specification phase.

Graphical modeling software tools have become commonplace in educational laboratories associated with courses in signal processing and communication systems. This paper explains that the purely graphical approach to modeling and simulation can be improved upon by combining graphical modeling with some low-level programming tasks. To implement this philosophy, a new software tool called EduCOM was developed. EduCOM offers a graphical modeling environment to construct system models. It also translates a graphical model into C/C++ code that can be compiled into an executable to simulate the model. Students are required to program the functionality of important blocks in their models such as filters, minimum-distance hard-decision rules, and look-up-tables. Anecdotal evidence is provided indicating that this approach offers an improved learning experience over pure graphical modeling approaches.

This paper proposes a novel architecture supporting dynamic load balancing on an FPGA for a molecular dynamics algorithm. Load balancing is primarily achieved through the use of specialized processing units, referred to as FLEX units. FLEX units are able to switch between tasks required by a molecular dynamics algorithm as often as needed in order to cater to the nature of the input parameters. This architecture is capable of run-time performance analysis and dynamic resource allocation in order to maximize throughput. Results of a prototype of the architecture targeting an FPGA are presented.

This paper presents an approach for dynamic software reconfiguration in sensor networks. Our approach utilizes explicit models of the design space of the embedded application. The design space is captured by formally modeling all the software components, their interfaces, and their composition. System requirements are expressed as formal constraints on QoS parameters that are measured at runtime. Reconfiguration is performed by transitioning from one point of the operation space to another based on the constraints. We demonstrate our approach using simulation results for a simple sensor network that performs one-dimensional tracking.

The design of modern high-performance embedded systems is challenging. Power and size constraints limit hardware size, while performance requirements demand algorithm-specific architectures. A model-integrated approach can be used in the design capture and synthesis of these systems. A domain-specific graphical system design environment allows the capture of system requirements, design information and alternatives, and of available processing resources in the form of models. A model interpretation process generates architecture specifications and compilable code. A typical first step in designing complex systems is to develop a simulation-based prototype. After functional verification of the prototype, system components are implemented, each tailored to a particular target platform. Only after all components are implemented can system integration be addressed, often uncovering inconsistencies between components and forcing costly redesign. This paper describes an extension of a model-integrated design environment and runtime system. Simulation-based components are included in system models. A simulation engine is interfaced to a runtime environment, allowing simulation components to run in the loop with nonsimulation based components. The extended environment allows prototype synthesis from system models and automates the integration of implemented components into the system. Further, the extended environment provides the designer with a powerful visualization and debugging tool.