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Security risk management for the Internet of things: Technologies and techniques for IoT security, privacy and data protection / John Soldatos (2020)  

Public Titre : Security risk management for the Internet of things: Technologies and techniques for IoT security, privacy and data protection Type de document : Monographie Auteurs : John Soldatos, Editeur scientifique Editeur : Boston, Delft : Now publishers Année de publication : 2020 Importance : 250 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-68083-682-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information [Termes descripteurs IGN] attaque informatique [Termes descripteurs IGN] cyberinfrastructure [Termes descripteurs IGN] données massives [Termes descripteurs IGN] données numériques [Termes descripteurs IGN] internet des objets [Termes descripteurs IGN] sécurité informatique Résumé : (éditeur) In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot. Note de contenu : 1- Introduction 2- Security Data Modelling for Configurable Risk Assessment as a Service in IoT Systems 3- Data-driven IoT Security Using Deep Learning Techniques 4- Privacy Awareness, Risk Assessment, and Control Measures in IoT Platforms: BRAIN-IoT Approach 5- IoT Network Risk Assessment and Mitigation: The SerIoT Approach 6- Chariot-integrated Approach to Safety, Privacy, and Security – CHARIOT IPSE 7- Pattern-driven Security, Privacy, Dependability and Interoperability in IoT 8- Enabling Continuous Privacy Risk Management in IoT Systems 9- Data Protection Compliance Requirements for the Internet of Things 10- Cybersecurity Certification in IoT Environments 11- Firmware Software Analysis at Source Code and Binary Levels 12- End-to-End Security for IoT 13- Blockchain Ledger Solution Affirming Physical, Operational, and Functional Changes in an IoT System 14- Leveraging Interledger Technologies in IoT Security Risk Management Numéro de notice : 25979 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Monographie DOI : 10.1561/9781680836837 En ligne : http://dx.doi.org/10.1561/9781680836837 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96707

SpiNNaker: A spiking neural network architecture / Steve Furber (2020)  

Public Titre : SpiNNaker: A spiking neural network architecture Type de document : Monographie Auteurs : Steve Furber, Editeur scientifique ; Petrut Bogdan, Editeur scientifique Editeur : Boston, Delft : Now publishers Année de publication : 2020 Importance : 352 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-68083-652-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Cerveau [Termes descripteurs IGN] outil logiciel [Termes descripteurs IGN] programmation stochastique [Termes descripteurs IGN] puce [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] système de traitement de l'information [Termes descripteurs IGN] vision par ordinateur Résumé : (éditeur) 20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come. Note de contenu : 1- Origins 2- The SpiNNaker Chip 3- Building SpiNNaker Machines 4- Stacks of Software Stacks 5- Applications - Doing Stuff on the Machine 6- From Activations to Spikes 7- Learning in Neural Networks 8- Creating the Future Numéro de notice : 25978 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie DOI : 10.1561/9781680836523 En ligne : http://dx.doi.org/10.1561/9781680836523 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96705