This open access book provides an overview about results of the InSecTT project. Artificial Intelligence of Things (AIoT) is the natural evolution for both Artificial Intelligence (AI) and Internet of Things (IoT) because they are mutually beneficial. AI increases the value of the IoT through machine learning by transforming the data into useful information, while the IoT increases the value of AI through connectivity and data exchange. Therefore, InSecTT—Intelligent Secure Trustable Things, a pan-European effort with over 50 key partners from 12 countries (EU and Turkey), provides intelligent, secure and trustworthy systems for industrial applications to provide comprehensive cost-efficient solutions of intelligent, end-to-end secure, trustworthy connectivity and interoperability to bring the Internet of Things and Artificial Intelligence together. InSecTT creates trust in AI-based intelligent systems and solutions as a major part of the AIoT. InSecTT fosters cooperation between big industrial players from various domains, a number of highly innovative SMEs distributed all over Europe and cutting-edge research organizations and universities. The project features a big variety of industry-driven use cases embedded into various application domains where Europe is in a leading position, i.e., smart infrastructure, building, manufacturing, automotive, aeronautics, railway, urban public transport, maritime as well as health. The demonstration of InSecTT solutions in well-known real-world environments like airports, trains, ports and the health sector shows their applicability on both high and broad level, going from citizens to European stakeholders. The first part of the book provides an introduction into the main topics of the InSecTT project: How to bring Internet of Things and Artificial Intelligence together to form the Artificial Intelligence of Things, a reference architecture for such kind of systems and how to develop trustworthy, ethical AI systems. In the second part, we show the development of essential technologies for creating trustworthy AIoT systems. The third part of the book is composed of a broad variety of examples on how to design, develop and validate trustworthy AIoT systems for industrial applications (including automotive, avionics, smart infrastructure, health care, manufacturing and railway).

This book constitutes the refereed proceedings of five International Workshops held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, virtually and in Hersonissos, Crete, Greece, in June 2022: the 11th Mining Humanistic Data Workshop (MHDW 2022); the 7th 5G-Putting Intelligence to the Network Edge Workshop (5G-PINE 2022); the 1st workshop on AI in Energy, Building and Micro-Grids (AIBMG 2022); the 1st Workshop/Special Session on Machine Learning and Big Data in Health Care (ML@HC 2022); and the 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics (AIBEI 2022). The 35 full papers presented at these workshops were carefully reviewed and selected from 74 submissions.

Wireless sensor networks have gained significant attention industrially and academically due to their wide range of uses in various fields. Because of their vast amount of applications, wireless sensor networks are vulnerable to a variety of security attacks. The protection of wireless sensor networks remains a challenge due to their resource-constrained nature, which is why researchers have begun applying several branches of artificial intelligence to advance the security of these networks. Research is needed on the development of security practices in wireless sensor networks by using smart technologies. Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks provides emerging research exploring the theoretical and practical advancements of security protocols in wireless sensor networks using artificial intelligence-based techniques. Featuring coverage on a broad range of topics such as clustering protocols, intrusion detection, and energy harvesting, this book is ideally designed for researchers, developers, IT professionals, educators, policymakers, practitioners, scientists, theorists, engineers, academicians, and students seeking current research on integrating intelligent techniques into sensor networks for more reliable security practices.

A cyber-physical system (CPS) is a computer system in which a mechanism is controlled or monitored by computer-based algorithms and involves transdisciplinary approaches, merging theories of cybernetics, mechatronics, design, and process science. This text mainly concentrates on offering a foundational theoretical underpinning, and a comprehensive and coherent review of intelligent security solutions for cyber-physical systems. Features: • Provides an overview of cyber-physical systems (CPSs) along with security concepts like attack detection methods, cyber-physical systems failures, and risk identification and management. • Showcases cyber-physical systems (CPSs) security solutions, lightweight cryptographic solutions, and CPS forensics, etc. • Emphasizes machine learning methods for behavior-based intrusion detection in cyber-physical systems (CPSs), resilient machine learning for networked CPS, fog computing industrial CPS, etc. • Elaborates classification of network abnormalities in Internet of Things-based cyber-physical systems (CPSs) using deep learning. • Includes case studies and applications in the domain of smart grid systems, industrial control systems, smart manufacturing, social network and gaming, electric power grid and energy systems, etc.

This book constitutes the refereed post-conference proceedings of the 7th EAI International Conference on Smart Objects and Technologies for social Good, GOODTECHS 2021, held in September 2021. Due to COVID-19 pandemic the conference was held virtually. The 24 full papers presented were selected from 53 submissions and issue design, implementation, deployment, operation, and evaluation of smart objects and technologies for social good. Social goods are products and services provided through private enterprises, government, or non-profit institutions and are related to healthcare, safety, sports, environment, democracy, computer science, and human rights. The papers are arranged in tracks on machine learning; IoT; social considerations of technology; technology and ageing; healthcare.

This book constitutes the refereed proceedings of the Chinese Conference on Trusted Computing and Information Security, CTCIS 2019, held in Shanghai, China, in October 2019. The 22 revised full papers presented were carefully reviewed and selected from 247 submissions. The papers are centered around cryptography, systems security, trusted computing, information security, network security, information hiding.