This book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyber-attacks and proposes deep learning-based approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of existing solutions to cover the entire IoT network security spectrum, the book utilizes artificial neural network models, which are used to classify, recognize, and model complex data including images, voice, and text, to enhance the level of security and privacy of IoT. This is applied to several IoT applications which include wireless sensor networks (WSN), meter reading transmission in smart grid, vehicular ad hoc networks (VANET), industrial IoT and connected networks. The book serves as a reference for researchers, academics, and network engineers who want to develop enhanced security and privacy features in the design of IoT systems.

Autorentext

Aaisha Makkar received her Bachelor of Computer Applications degree from Panjab University, Chandigarh, India, in 2010 and Master of Computer Applications from National Institute of Technology (NIT), Kurukshetra, India, in 2013. She had worked as an assistant professor in Computer Application Department of NIT, Kurukshetra. She is currently pursuing her Ph.D. degree from Computer Science and Engineering Department in Thapar Institute of Engineering and Technology, Patiala (Punjab), India. Her research interests include data mining, web mining, algorithms, machine learning, and Internet of things.

Prof. Neeraj Kumar (SM'17) received his Ph.D. in CSE from Shri Mata Vaishno Devi University, Katra (Jammu and Kashmir), India, in 2009, and was a postdoctoral research fellow in Coventry University, Coventry, UK. He is working as a professor in the Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala (Pb.), India. He has published more than 400 technical research papers in top-cited journals such as IEEE TKDE, IEEE TIE, IEEE TDSC, IEEE TITS, IEEE TCE, IEEE TII, IEEE TVT, IEEE ITS, IEEE SG, IEEE Netw., IEEE Comm., IEEE WC, IEEE IoTJ, IEEE SJ, Computer Networks, Information sciences, FGCS, JNCA, JPDC, and ComCom. He has guided many research scholars leading to Ph.D. and M.E./M.Tech. His research is supported by funding from UGC, DST, CSIR, and TCS. He is an associate technical editor of IEEE Communication Magazine. He is an associate editor of IJCS, Wiley, JNCA, Elsevier, Elsevier Computer Communications, and Security and Communication, Wiley. He has been a guest editor of various International Journals of repute such as-IEEE Access, IEEE Communication Magazine, IEEE Network Magazine, Computer Networks, Elsevier, Future Generation Computer Systems, Elsevier, and Journal of Medical Systems, Springer, Computer and Electrical Engineering, Elsevier, Mobile Information Systems, International Journal of Ad hoc and Ubiquitous Computing, Telecommunication Systems, Springer, and Journal of Supercomputing, Springer. He has been a workshop chair at IEEE Globecom 2018 and IEEE ICC 2019 and TPC Chair and a member for various International conferences. He is senior member of the IEEE. He has more than 9406 citations to his credit with current h-index of 53. He has won the best papers award from IEEE Systems Journal and ICC 2018, Kansas City, in 2018. He is a visiting research fellow at Coventry University, Newcastle University, UK.

Inhalt

Metamorphosis of Industrial IoT using Deep Leaning.- Deep Learning Models and their Architectures for Computer Vision Applications: A Review.- IoT Data Security with Machine Learning Blockchain: Risks and Countermeasures.- A Review on Cyber Crimes on the Internet of Things.- Deep learning framework for anomaly detection in IoT enabled systems.- Anomaly Detection using Unsupervised Machine Learning Algorithms.- Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT.- Deep Learning based security preservation of IoT: An industrial machine health monitoring scenario.- Deep learning Models: An Understandable Interpretable Approaches.