Formalizing Cyber--Physical System Model Transformation via Abstract Interpretation

Model transformation tools assist system designers by reducing the labor--intensive task of creating and updating models of various aspects of systems, ensuring that modeling assumptions remain consistent across every model of a system, and identifying constraints on system design imposed by these modeling assumptions. We have proposed a model transformation approach based on abstract interpretation, a static program analysis technique. Abstract interpretation allows us to define transformations that are provably correct and specific. This work develops the foundations of this approach to model transformation. We define model transformation in terms of abstract interpretation and prove the soundness of our approach. Furthermore, we develop formalisms useful for encoding model properties. This work provides a methodology for relating models of different aspects of a system and for applying modeling techniques from one system domain, such as smart power grids, to other domains, such as water distribution networks.Comment: 8 pages, 4 figures; to appear in HASE 2019 proceeding

Securing The Transportation Of Tomorrow: Enabling Self-Healing Intelligent Transportation

The safety of autonomous vehicles relies on dependable and secure infrastructure for intelligent transportation. The doctoral research described in this paper aims to enable self-healing and survivability of the intelligent transportation systems required for autonomous vehicles (AV-ITS). The proposed approach is comprised of four major elements: qualitative and quantitative modeling of the AV-ITS, stochastic analysis to capture and quantify interdependencies, mitigation of disruptions, and validation of efficacy of the self-healing process. This paper describes the overall methodology and presents preliminary results, including an agent-based model for detection of and recovery from disruptions to the AV-ITS

Metrics -guided models and methods for cost and quality management of component -based software

The paradigm shift to commercial off-the-shelf software components appears inevitable, necessitating drastic changes to current software development and evolution practices. Lack of confidence in the quality of third-party software components and the systems based on them has currently limited the applicability of this efficient paradigm to non-critical applications. Software metrics can be used to objectively quantify the quality of software components and systems, alleviating quality and risk concerns and raising assurance in component-based systems. This research identifies a set of software metrics pertinent to cost and quality management of component-based systems. To determine the extent of testing required for quality assurance, a temporal model is developed for predicting the value of test coverage, which is one of the proposed metrics. The metrics are then utilized in developing a graphical model for characterization of a component-based software system based on quality attributes of its constituent components and integration code. Based on this model, a development methodology is proposed for guiding acquisition and integration efforts by using multi-objective optimization to select components that will yield the highest quality within affordable cost. Enterprise integration, which aims at aligning business operations and information technology resources in an enterprise, is an emerging application of the component-based paradigm, and well-suited to metrics-guided optimization. As a final contribution of the research, the aforementioned techniques are applied to cost and quality management of enterprise integration

Algorithmic Support for Personalized Course Selection and Scheduling

The work presented in this paper demonstrates the use of context-aware recommendation to facilitate personalized education, by assisting students in selecting courses and course content and mapping a trajectory to graduation. The recommendation algorithm considers a student\u27s profile and their program\u27s curricular requirements in generating a schedule of courses, while aiming to reduce attributes such as cost and time-to-degree. The resulting optimization problem is solved using integer linear programming and graph-based heuristics. The course selection algorithm has been developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with assurance that recommended selections are always valid

Wheel Tracks, Rutting a New Oregon Trail: A Survey of Autonomous Vehicle Cybersecurity and Survivability Analysis Research

The Rapid Development of Autonomous Vehicles during the Past Decade Has Caused Increasingly Grave Cybersecurity Challenges to Be Associated with their Use. among These Challenges Are Vulnerabilities Involving Existing Vehicular Technology, Which Have Been Subject to Well-Publicized Exploits that Bring into Question the Survivability of These Vehicles under Failure or Attack. This Chapter is a Survey of the Research Landscape of Autonomous Vehicles, Focusing on Security and Survivability; Related Attributes Such as Performability Are Also Considered. Research Areas Are Visualized in a Taxonomy and Gaps Are Discussed throughout the Paper. We Conclude with Recommendations and a Discussion of Future Research Opportunities

Survivability Evaluation of Gas, Water and Electricity Infrastructures

AbstractThe infrastructures used in cities to supply power, water and gas are consistently becoming more automated. As society depends critically on these cyber-physical infrastructures, their survivability assessment deserves more attention. In this overview, we first touch upon a taxonomy on survivability of cyber-physical infrastructures, before we focus on three classes of infrastructures (gas, water and electricity) and discuss recent modelling and evaluation approaches and challenges