The book presents the proceedings of four conferences: The 19th International Conference on Security & Management (SAM'20), The 19th International Conference on Wireless Networks (ICWN'20), The 21st International Conference on Internet Computing & Internet of Things (ICOMP'20), and The 18th International Conference on Embedded Systems, Cyber-physical Systems (ESCS'20). The conferences took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Authors include academics, researchers, professionals, and students. Presents the proceedings of four conferences as part of the 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20); Includes the tracks on security & management, wireless networks, internet computing and IoT, and embedded systems as well as cyber-physical systems; Features papers from SAM’20, ICWN’20, ICOMP’20 and ESCS’20.

This book presents a systematic and comprehensive overview for IoT security. It first introduces architecture approaches for IoT and IoT security, describing the security techniques for different layers in the IoT security architecture. It also provides an in-depth analysis on the difference between IoT security and traditional system and data security. It is commonly known that information security includes data confidentiality, data integrity, and availability, and that measures include non-repudiation and access control. However, in practical IoT system construction, many more security measures need to be carefully considered. As such, this book presents around 60 different security measures, mainly focusing on the sensor layer of IoT. These security measures can serve as a source of reference for IoT system construction, as well as IoT security standard making.

Most of the devices in the Internet of Things will be battery powered sensor devices. All the operations done on battery powered devices require minimum computation. Secure algorithms like RSA become useless in the Internet of Things environment. Elliptic curve based cryptography emerges as a best solution for this problem because it provides higher security in smaller key size compare to RSA. This book focuses on the use of Elliptic Curve Cryptography with different authentication architectures and authentication schemes using various security algorithms. It also includes a review of the math required for security and understanding Elliptic Curve Cryptography.

SECURITY AND PRIVACY IN THE INTERNET OF THINGS Provides the authoritative and up-to-date information required for securing IoT architecture and applications The vast amount of data generated by the Internet of Things (IoT) has made information and cyber security vital for not only personal privacy, but also for the sustainability of the IoT itself. Security and Privacy in the Internet of Things brings together high-quality research on IoT security models, architectures, techniques, and application domains. This concise yet comprehensive volume explores state-of-the-art mitigations in IoT security while addressing important security and privacy challenges across different IoT layers. The book provides timely coverage of IoT architecture, security technologies and mechanisms, and applications. The authors outline emerging trends in IoT security and privacy with a focus on areas such as smart environments and e-health. Topics include authentication and access control, attack detection and prevention, securing IoT through traffic modeling, human aspects in IoT security, and IoT hardware security. Presenting the current body of knowledge in a single volume, Security and Privacy in the Internet of Things: Discusses a broad range of IoT attacks and defense mechanisms Examines IoT security and privacy protocols and approaches Covers both the logical and physical security of IoT devices Addresses IoT security through network traffic modeling Describes privacy preserving techniques in smart cities Explores current threat and vulnerability analyses Security and Privacy in the Internet of Things: Architectures, Techniques, and Applications is essential reading for researchers, industry practitioners, and students involved in IoT security development and IoT systems deployment.

As technology continues to expand and develop, the internet of things (IoT) is playing a progressive role in the infrastructure of electronics. The increasing amount of IoT devices, however, has led to the emergence of significant privacy and security challenges. Security and Privacy Issues in Sensor Networks and IoT is a collection of innovative research on the methods and applications of protection disputes in the internet of things and other computing structures. While highlighting topics that include cyber defense, digital forensics, and intrusion detection, this book is ideally designed for security analysts, IT specialists, software developers, computer engineers, industry professionals, academicians, students, and researchers seeking current research on defense concerns in cyber physical systems.

Security and Privacy Issues in IoT Devices and Sensor Networks investigates security breach issues in IoT and sensor networks, exploring various solutions. The book follows a two-fold approach, first focusing on the fundamentals and theory surrounding sensor networks and IoT security. It then explores practical solutions that can be implemented to develop security for these elements, providing case studies to enhance understanding. Machine learning techniques are covered, as well as other security paradigms, such as cloud security and cryptocurrency technologies. The book highlights how these techniques can be applied to identify attacks and vulnerabilities, preserve privacy, and enhance data security. This in-depth reference is ideal for industry professionals dealing with WSN and IoT systems who want to enhance the security of these systems. Additionally, researchers, material developers and technology specialists dealing with the multifarious aspects of data privacy and security enhancement will benefit from the book's comprehensive information. - Provides insights into the latest research trends and theory in the field of sensor networks and IoT security - Presents machine learning-based solutions for data security enhancement - Discusses the challenges to implement various security techniques - Informs on how analytics can be used in security and privacy

Internet of Things: Challenges, Advances, and Applications provides a comprehensive introduction to IoT, related technologies, and common issues in the adoption of IoT on a large scale. It surveys recent technological advances and novel solutions for challenges in the IoT environment. Moreover, it provides detailed discussion of the utilization of IoT and its underlying technologies in critical application areas, such as smart grids, healthcare, insurance, and the automotive industry. The chapters of this book are authored by several international researchers and industry experts. This book is composed of 18 self-contained chapters that can be read, based on interest. Features: Introduces IoT, including its history, common definitions, underlying technologies, and challenges Discusses technological advances in IoT and implementation considerations Proposes novel solutions for common implementation issues Explores critical application domains, including large-scale electric power distribution networks, smart water and gas grids, healthcare and e-Health applications, and the insurance and automotive industries The book is an excellent reference for researchers and post-graduate students working in the area of IoT, or related areas. It also targets IT professionals interested in gaining deeper knowledge of IoT, its challenges, and application areas.

This book presents state-of-the-art research on security and privacy- preserving for IoT and 5G networks and applications. The accepted book chapters covered many themes, including traceability and tamper detection in IoT enabled waste management networks, secure Healthcare IoT Systems, data transfer accomplished by trustworthy nodes in cognitive radio, DDoS Attack Detection in Vehicular Ad-hoc Network (VANET) for 5G Networks, Mobile Edge-Cloud Computing, biometric authentication systems for IoT applications, and many other applications It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this particular area or those interested in grasping its diverse facets and exploring the latest advances on security and privacy- preserving for IoT and 5G networks.

This book presents refereed proceedings of the Third International Conference on Advances in Cyber Security, ACeS 2021, held in Penang, Malaysia, in August 2021. The 36 full papers were carefully reviewed and selected from 92 submissions. The papers are organized in the following topical sections: Internet of Things, Industry 4.0 and Blockchain, and Cryptology; Digital Forensics and Surveillance, Botnet and Malware, DDoS, and Intrusion Detection/Prevention; Ambient Cloud and Edge Computing, SDN, Wireless and Cellular Communication; Governance, Social Media, Mobile and Web, Data Privacy, Data Policy and Fake News.

Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware – to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security.