4,957 research outputs found

    Multi-Layer Cyber-Physical Security and Resilience for Smart Grid

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    The smart grid is a large-scale complex system that integrates communication technologies with the physical layer operation of the energy systems. Security and resilience mechanisms by design are important to provide guarantee operations for the system. This chapter provides a layered perspective of the smart grid security and discusses game and decision theory as a tool to model the interactions among system components and the interaction between attackers and the system. We discuss game-theoretic applications and challenges in the design of cross-layer robust and resilient controller, secure network routing protocol at the data communication and networking layers, and the challenges of the information security at the management layer of the grid. The chapter will discuss the future directions of using game-theoretic tools in addressing multi-layer security issues in the smart grid.Comment: 16 page

    Risks, Safety and Security in the Ecosystem of Smart Cities

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    We have performed a review of systemic risks in smart cities dependent on intelligent and partly autonomous transport systems. Smart cities include concepts such as smart transportation/use of autonomous transportation systems (i.e., autonomous cars, subways, shipping, drones) and improved management of infrastructure (power and water supply). At the same time, this requires safe and resilient infrastructures and need for global collaboration. One challenge is some sort of risk based regulation of emergent vulnerabilities. In this paper we focus on emergent vulnerabilities and discussion of how mitigation can be organized and structured based on emergent and known scenarios cross boundaries. We regard a smart city as a software ecosystem (SEC), defined as a dynamic evolution of systems on top of a common technological platform offering a set of software solutions and services. Software ecosystems are increasingly being used to support critical tasks and operations. As a part of our work we have performed a systematic literature review of safety, security and resilience software ecosystems, in the period 2007–2016. The perspective of software ecosystems has helped to identify and specify patterns of safety, security and resilience on a relevant abstraction level. Significant vulnerabilities and poor awareness of safety, security and resilience has been identified. Key actors that should increase their attention are vendors, regulators, insurance companies and the research community. There is a need to improve private-public partnership and to improve the learning loops between computer emergency teams, security information providers (SIP), regulators and vendors. There is a need to focus more on safety, security and resilience and to establish regulations of responsibilities on the vendors for liabilities

    Software engineering for AI-based systems: A survey

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    AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state-of-the-art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.This work has been partially funded by the “Beatriz Galindo” Spanish Program BEAGAL18/00064 and by the DOGO4ML Spanish research project (ref. PID2020-117191RB-I00)Peer ReviewedPostprint (author's final draft

    Characterizing the Identity of Model-based Safety Assessment: A Systematic Analysis

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    Model-based safety assessment has been one of the leading research thrusts of the System Safety Engineering community for over two decades. However, there is still a lack of consensus on what MBSA is. The ambiguity in the identity of MBSA impedes the advancement of MBSA as an active research area. For this reason, this paper aims to investigate the identity of MBSA to help achieve a consensus across the community. Towards this end, we first reason about the core activities that an MBSA approach must conduct. Second, we characterize the core patterns in which the core activities must be conducted for an approach to be considered MBSA. Finally, a recently published MBSA paper is reviewed to test the effectiveness of our characterization of MBSA

    Autonomic Computing

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    Autonomic computing (AC) has as its vision the creation of self-managing systems to address today’s con-cerns of complexity and total cost of ownership while meeting tomorrow’s needs for pervasive and ubiquitous computation and communication. This paper reports on the latest auto-nomic systems research and technologies to influence the industry; it looks behind AC, summarising what it is, the current state-of-the-art research, related work and initiatives, highlights research and technology transfer issues and concludes with further and recommended reading

    Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1

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    This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
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