An unfortunate outcome of the growth of the Internet and mobile technologies has been the challenge of countering cybercrime. This book introduces and explains the latest trends and techniques of edge artificial intelligence (EdgeAI) intended to help cyber security experts design robust cyber defense systems (CDS), including host-based and network-based intrusion detection system and digital forensic intelligence. This book discusses the direct confluence of EdgeAI with big data, as well as demonstrating detailed reviews of recent cyber threats and their countermeasure. It provides computational intelligence techniques and automated reasoning models capable of fast training and timely data processing of cyber security big data, in addition to other basic information related to network security. In addition, it provides a brief overview of modern cyber security threats and outlines the advantages of using EdgeAI to counter these threats, as well as exploring various cyber defense mechanisms (CDM) based on detection type and approaches. Specific challenging areas pertaining to cyber defense through EdgeAI, such as improving digital forensic intelligence, proactive and adaptive defense of network infrastructure, and bio-inspired CDM, are also discussed. This book is intended as a reference for academics and students in the field of network and cybersecurity, particularly on the topics of intrusion detection systems, smart grid, EdgeAI, and bio-inspired cyber defense principles. The front-line EdgeAI techniques discussed will also be of use to cybersecurity engineers in their work enhancing cyber defense systems.
Ranjit Panigrahi is an Assistant Professor at the Department of Computer Applications, Sikkim Manipal University. He has been actively involved in numerous conferences and serves as a member of the technical review committee for international journals published by Springer Nature and Inderscience. His research interests are Machine Learning, Pattern Recognition and Wireless Sensor Networks.
Victor Hugo C. de Albuquerque is a professor and senior researcher at the University of Fortaleza, LAPISCO/IFCE, and ARMTEC Tecnologia em Robótica, Brazil. He specialises in the Internet of Things, Machine/Deep Learning, Pattern Recognition and Robotics. His work has been funded by the Brazilian National Council for Research and Development.
Akash Kumar Bhoi is an Assistant Professor (Research) at the Department of Electrical and Electronics Engineering at Sikkim Manipal Institute of Technology (SMIT). He is a member of IEEE, ISEIS, and IAENG, an associate member of IEI, UACEE, and editorial board member reviewer of Indian and international journals. His research interests are Biomedical Signal Processing, Internet of Things, Computational Intelligence, Antenna and Renewable Energy.
Hareesha K. S. is a Professor at the Department of Computer Applications at Manipal Institute of Technology, MAHE. He has received fellowship awards from the National Science Foundation, USA and Federation University, Australia and was recently selected for AICTE-UKIERI Technical Leadership Development Programme for his research and academic contributions. His research interests are improving machine learning algorithms and understanding, design of intelligent soft computing models in digital image processing and data mining. He is also works on Virtual Reality and Augmented Reality for medical surgery planning.
Dr. P Naga Srinivasu is an Associate Professor in the Department of Computer Science at Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, India. Holding a post-doctoral fellowship from the Department of Teleinformatics Engineering at the Federal University of Ceará, Brazil, he also serves as a research fellow at INTI International University, Malaysia. After graduating with a Bachelor's degree in Computer Science Engineering from SSIET, JNTU Kakinada, in 2011, he obtained a Master's in Computer Science Technology from GITAM University, Visakhapatnam, in 2013. His doctoral research at GITAM University focused on Automatic Segmentation Methods for Volumetric Estimation of Damaged Areas in Astrocytoma instances Identified from 2D Brain MR Imaging. His diverse contributions reflect a steadfast dedication to advancing research and knowledge in healthcare informatics and biomedical engineering.
