116 research outputs found

    Dependability for declarative mechanisms: neural networks in autonomous vehicles decision making.

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    Despite being introduced in 1958, neural networks appeared in numerous applications of different fields in the last decade. This change was possible thanks to the reduced costs of computing power required for deep neural networks, and increasing available data that provide examples for training sets. The 2012 ImageNet image classification competition is often used as a example to describe how neural networks became at this time good candidates for applications: during this competition a neural network based solution won for the first time. In the following editions, all winning solutions were based on neural networks. Since then, neural networks have shown great results in several non critical applications (image recognition, sound recognition, text analysis, etc...). There is a growing interest to use them in critical applications as their ability to generalize makes them good candidates for applications such as autonomous vehicles, but standards do not allow that yet. Autonomous driving functions are currently researched by the industry with the final objective of producing in the near future fully autonomous vehicles, as defined by the fifth level of the SAE international (Society of Automotive Engineers) classification. Autonomous driving process is usually decomposed into four different parts: the where sensors get information from the environment, the where the data from the different sensors is merged into one representation of the environment, the that uses the representation of the environment to decide what should be the vehicles behavior and the commands to send to the actuators and finally the part that implements these commands. In this thesis, following the interest of the company Stellantis, we will focus on the decision part of this process, considering neural network based solution. Automotive being a safety critical application, it is required to implement and ensure the dependability of the systems, and this is why neural networks use is not allowed at the moment: their lack of safety forbid their use in such applications. Dependability methods for classical software systems are well known, but neural networks do not have yet similar dependable mechanisms to guarantee their trust. This problem is due to several reasons, among them the difficulty to test applications with a quasi-infinite operational domain and whose functions are hard to define exhaustively in the specifications. Here we can find the motivation of this thesis: how can we ensure the dependability of neural networks in the context of decision for autonomous vehicles? Research is now being conducted on the topic of dependability and safety of neural networks with several approaches being considered and our research is motivated by the great potential in safety critical applications mentioned above. In this thesis, we will focus on one category of method that seems to be a good candidate to ensure the dependability of neural networks by solving some of the problems of testing: the formal verification for neural networks. These methods aim to prove that a neural network respects a safety property on an entire range of its input and output domains. Formal verification is already used in other domains and is seen as a trusted method to give confidence in a system, but it remains for the moment a research topic for neural networks with currently no industrial applications. The main contributions of this thesis are the following: a proposal of a characterization of neural network from a software development perspective, and a corresponding classification of their faults, errors and failures, the identification of a potential threat to the use of formal verification. This threat is the erroneous neural network model problem, that may lead to trust a formally validated safety property that does not hold in real life, the realization of an experiment that implements a formal verification for neural networks in an autonomous driving application that is to the best of our knowledge the closest to industrial use. For this application, we chose to work with an ACC (Adaptive Cruise Control) function, which is an autonomous driving function that performs the longitudinal control of a vehicle. The experiment is conducted with the use of a simulator and a neural network formal verification tool. The other contributions of the thesis are the following: theoretical example of the erroneous neural network model problem and a practical example in our autonomous driving experiment, a proposal of detection and recovery mechanisms as a solution to the erroneous model problem mentioned above, an implementation of these detection and recovery mechanisms in our autonomous driving experiment and a discussion about difficulties and possible processes for the implementation of formal verification for neural networks that we developed during our experiments

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

    Logs and Models in Engineering Complex Embedded Production Software Systems

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    Modeling Deception for Cyber Security

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    In the era of software-intensive, smart and connected systems, the growing power and so- phistication of cyber attacks poses increasing challenges to software security. The reactive posture of traditional security mechanisms, such as anti-virus and intrusion detection systems, has not been sufficient to combat a wide range of advanced persistent threats that currently jeopardize systems operation. To mitigate these extant threats, more ac- tive defensive approaches are necessary. Such approaches rely on the concept of actively hindering and deceiving attackers. Deceptive techniques allow for additional defense by thwarting attackers’ advances through the manipulation of their perceptions. Manipu- lation is achieved through the use of deceitful responses, feints, misdirection, and other falsehoods in a system. Of course, such deception mechanisms may result in side-effects that must be handled. Current methods for planning deception chiefly portray attempts to bridge military deception to cyber deception, providing only high-level instructions that largely ignore deception as part of the software security development life cycle. Con- sequently, little practical guidance is provided on how to engineering deception-based techniques for defense. This PhD thesis contributes with a systematic approach to specify and design cyber deception requirements, tactics, and strategies. This deception approach consists of (i) a multi-paradigm modeling for representing deception requirements, tac- tics, and strategies, (ii) a reference architecture to support the integration of deception strategies into system operation, and (iii) a method to guide engineers in deception mod- eling. A tool prototype, a case study, and an experimental evaluation show encouraging results for the application of the approach in practice. Finally, a conceptual coverage map- ping was developed to assess the expressivity of the deception modeling language created.Na era digital o crescente poder e sofisticação dos ataques cibernéticos apresenta constan- tes desafios para a segurança do software. A postura reativa dos mecanismos tradicionais de segurança, como os sistemas antivírus e de detecção de intrusão, não têm sido suficien- tes para combater a ampla gama de ameaças que comprometem a operação dos sistemas de software actuais. Para mitigar estas ameaças são necessárias abordagens ativas de defesa. Tais abordagens baseiam-se na ideia de adicionar mecanismos para enganar os adversários (do inglês deception). As técnicas de enganação (em português, "ato ou efeito de enganar, de induzir em erro; artimanha usada para iludir") contribuem para a defesa frustrando o avanço dos atacantes por manipulação das suas perceções. A manipula- ção é conseguida através de respostas enganadoras, de "fintas", ou indicações erróneas e outras falsidades adicionadas intencionalmente num sistema. É claro que esses meca- nismos de enganação podem resultar em efeitos colaterais que devem ser tratados. Os métodos atuais usados para enganar um atacante inspiram-se fundamentalmente nas técnicas da área militar, fornecendo apenas instruções de alto nível que ignoram, em grande parte, a enganação como parte do ciclo de vida do desenvolvimento de software seguro. Consequentemente, há poucas referências práticas em como gerar técnicas de defesa baseadas em enganação. Esta tese de doutoramento contribui com uma aborda- gem sistemática para especificar e desenhar requisitos, táticas e estratégias de enganação cibernéticas. Esta abordagem é composta por (i) uma modelação multi-paradigma para re- presentar requisitos, táticas e estratégias de enganação, (ii) uma arquitetura de referência para apoiar a integração de estratégias de enganação na operação dum sistema, e (iii) um método para orientar os engenheiros na modelação de enganação. Uma ferramenta protó- tipo, um estudo de caso e uma avaliação experimental mostram resultados encorajadores para a aplicação da abordagem na prática. Finalmente, a expressividade da linguagem de modelação de enganação é avaliada por um mapeamento de cobertura de conceitos

    A Framework for Seamless Variant Management and Incremental Migration to a Software Product-Line

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    Context: Software systems often need to exist in many variants in order to satisfy varying customer requirements and operate under varying software and hardware environments. These variant-rich systems are most commonly realized using cloning, a convenient approach to create new variants by reusing existing ones. Cloning is readily available, however, the non-systematic reuse leads to difficult maintenance. An alternative strategy is adopting platform-oriented development approaches, such as Software Product-Line Engineering (SPLE). SPLE offers systematic reuse, and provides centralized control, and thus, easier maintenance. However, adopting SPLE is a risky and expensive endeavor, often relying on significant developer intervention. Researchers have attempted to devise strategies to synchronize variants (change propagation) and migrate from clone&own to an SPL, however, they are limited in accuracy and applicability. Additionally, the process models for SPLE in literature, as we will discuss, are obsolete, and only partially reflect how adoption is approached in industry. Despite many agile practices prescribing feature-oriented software development, features are still rarely documented and incorporated during actual development, making SPL-migration risky and error-prone.Objective: The overarching goal of this PhD is to bridge the gap between clone&own and software product-line engineering in a risk-free, smooth, and accurate manner. Consequently, in the first part of the PhD, we focus on the conceptualization, formalization, and implementation of a framework for migrating from a lean architecture to a platform-based one.Method: Our objectives are met by means of (i) understanding the literature relevant to variant-management and product-line migration and determining the research gaps (ii) surveying the dominant process models for SPLE and comparing them against the contemporary industrial practices, (iii) devising a framework for incremental SPL adoption, and (iv) investigating the benefit of using features beyond PL migration; facilitating model comprehension.Results: Four main results emerge from this thesis. First, we present a qualitative analysis of the state-of-the-art frameworks for change propagation and product-line migration. Second, we compare the contemporary industrial practices with the ones prescribed in the process models for SPL adoption, and provide an updated process model that unifies the two to accurately reflect the real practices and guide future practitioners. Third, we devise a framework for incremental migration of variants into a fully integrated platform by exploiting explicitly recorded metadata pertaining to clone and feature-to-asset traceability. Last, we investigate the impact of using different variability mechanisms on the comprehensibility of various model-related tasks.Future work: As ongoing and future work, we aim to integrate our framework with existing IDEs and conduct a developer study to determine the efficiency and effectiveness of using our framework. We also aim to incorporate safe-evolution in our operators

    EG-ICE 2021 Workshop on Intelligent Computing in Engineering

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    The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

    Digital Transformation in Healthcare

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    This book presents a collection of papers revealing the impact of advanced computation and instrumentation on healthcare. It highlights the increasing global trend driving innovation for a new era of multifunctional technologies for personalized digital healthcare. Moreover, it highlights that contemporary research on healthcare is performed on a multidisciplinary basis comprising computational engineering, biomedicine, biomedical engineering, electronic engineering, and automation engineering, among other areas
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