This volume is an initiative undertaken by the IEEE Computational Intelligence Society’s Task Force on Security, Surveillance and Defense to consolidate and disseminate the role of CI techniques in the design, development and deployment of security and defense solutions. Applications range from the detection of buried explosive hazards in a battlefield to the control of unmanned underwater vehicles, the delivery of superior video analytics for protecting critical infrastructures or the development of stronger intrusion detection systems and the design of military surveillance networks. Defense scientists, industry experts, academicians and practitioners alike will all benefit from the wide spectrum of successful applications compiled in this volume. Senior undergraduate or graduate students may also discover uncharted territory for their own research endeavors.

In the ever-accelerating tapestry of our digital age, the symbiotic relationship between computational intelligence and cyber security has become the linchpin of progress. The relentless pace of technological evolution and the ceaseless emergence of cyber threats demand not only adaptation but also an exploration of the forefronts of innovation and defence. Recent Advances in Computational Intelligence and Cyber security is a testament to the exhilarating journey undertaken by researchers, practitioners, and visionaries in these pivotal fields. Within the confines of this book, we embark on a captivating exploration of the cutting-edge developments that define the current state of computational intelligence and the intricate dance with the ever-evolving landscape of cyber security.

This book presents research reports selected to indicate the state of the art in intelligent and database systems and to promote new research in this field. It includes 34 chapters based on original research presented as posters at the 11th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2019), held in Yogyakarta, Indonesia on 8–11 April 2019. The increasing use of intelligent and database systems in various fields, such as industry, medicine and science places those two elements of computer science among the most important directions of research and application, which currently focuses on such key technologies as machine learning, cloud computing and processing of big data. It is estimated that further development of intelligent systems and the ability to gather, store and process enormous amounts of data will be needed to solve a number of crucial practical and theoretical problems. The book is divided into five parts: (a) Sensor Clouds and Internet of Things, (b) Machine Learning and Decision Support Systems, (c) Computer Vision Techniques and Applications, (d) Intelligent Systems in Biomedicine, and (e) Applications of Intelligent Information Systems. It is a valuable resource for researchers and practitioners interested in increasing the synergy between artificial intelligence and database technologies, as well as for graduate and Ph.D. students in computer science and related fields.

COMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more. The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management and security of IT systems and networks. Readers will also enjoy topics like: A thorough introduction to network and service management, machine learning, and artificial intelligence An exploration of artificial intelligence and machine learning for management models, including autonomic management, policy-based management, intent based ­management, and network virtualization-based management Discussions of AI and ML for architectures and frameworks, including cloud ­systems, software defined networks, 5G and 6G networks, and Edge/Fog networks An examination of AI and ML for service management, including the automatic ­generation of workload profiles using unsupervised learning Perfect for information and communications technology educators, Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning will also earn a place in the libraries of engineers and professionals who seek a structured reference on how the emergence of artificial intelligence and machine learning techniques is affecting service and network management.

Today’s real-world problems and applications in sensory systems and target detection require efficient, comprehensive and fault-tolerant multi-sensor allocation. This book presents the theory and applications of novel methods developed for such sophisticated systems. It discusses the advances in multi-agent systems and AI along with collaborative control theory and tools. Further, it examines the formulation and development of an allocation framework for heterogeneous multi-sensor systems for various real-world problems that require sensors with different performances to allocate multiple tasks, with unknown a priori priorities that arrive at unknown locations at unknown time. It demonstrates how to decide which sensor to allocate to which tasks when and where. Lastly, it explains the reliability and availability issues of task allocation systems, and includes methods for their optimization. The presented methods are explained, measured, and evaluated by extensive simulations, and the results of these simulations are presented in this book. This book is an ideal resource for academics, researchers and graduate students as well as engineers and professionals and is relevant for various applications such as sensor network design, multi-agent systems, task allocation, target detection, and team formation.

Within this context, big data analytics (BDA) can be an important tool given that many analytic techniques within the big data world have been created specifically to deal with complexity and rapidly changing conditions. The important task for public sector organizations is to liberate analytics from narrow scientific silos and expand it across internally to reap maximum benefit across their portfolios of programs. This book highlights contextual factors important to better situating the use of BDA within government organizations and demonstrates the wide range of applications of different BDA techniques. It emphasizes the importance of leadership and organizational practices that can improve performance. It explains that BDA initiatives should not be bolted on but should be integrated into the organization’s performance management processes. Equally important, the book includes chapters that demonstrate the diversity of factors that need to be managed to launch and sustain BDA initiatives in public sector organizations.

This book constitutes the refereed proceedings of the 13th International Conference on Information Systems Security, ICISS 2017, held in Mumbai, India, in December 2017. The 17 revised full papers and 7 short papers presented together with 2 invited papers were carefully reviewed and selected from 73 submissions. The papers address the following topics: privacy/cryptography, systems security, security analysis, identity management and access control, security attacks and detection, network security.

This book constitutes the refereed proceedings of the Second International Conference on Security and Privacy, ISEA-ISAP 2018, held in Jaipur, India, in January 2019. The conference was originally planned to be held in 2018 which is why the acronym contains "2018". The 21 revised full papers presented were carefully reviewed and selected from 87 submissions. The papers are organized in topical sections: authentication and access control, malware analysis, network security, privacy preservation, secure software systems and social network analytics.

This paper will focus on the process of “fusing” several observations or models of uncertainty into a single resultant model. Many existing approaches to fusion use subjective quantities such as “strengths of belief” and process these quantities with heuristic algorithms. This paper argues in favor of quantities that can be objectively measured, as opposed to the subjective “strength of belief” values. This paper will focus on probability distributions, and more importantly, structures that denote sets of probability distributions known as “credal sets”. The novel aspect of this paper will be a taxonomy of models of fusion that use specific types of credal sets, namely probability interval distributions and Dempster-Shafer models.