Big Data and Edge Intelligence for Enhanced Cyber Defense (eBook, PDF)

Principles and Research
Redaktion: Rani Panigrahi, Chhabi; K. S., Hareesha; Kumar Bhoi, Akash; de Albuquerque, Victor Hugo C.

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 200
• Erscheinungstermin: 31. Juli 2024
• Englisch
• ISBN-13: 9781040048368
• Artikelnr.: 72284992

Autorenporträt

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.

Inhaltsangabe

1. Challenges, Existing Strategies, and New Barriers in IoT
Vulnerability Assessment for Sustainable Computing
2. AI AND IOT BASED INTRUSION DETECTION SYSTEM FOR CYBERSECURITY
3. Advancing Digital Forensic Intelligence: Leveraging EdgeAI Techniques
for Real-time Threat Detection and Privacy Protection
4. ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN OVER EDGE FOR SUSTAINABLE
SMART CITIES
5. Enhancing Intrusion Detection in IoT-based Vulnerable Environments
using Federated Learning
6. Effective Intrusion Detection in High-Class Imbalance Networks Using
Consolidated Tree Construction
7. Internet of Things intrusion detection system: A systematic study of
Artificial Intelligence, Deep Learning and Machine Learning
approaches