Security Framework and Defense Mechanisms for IoT Reactive Jamming Attacks

This book digs into the important confluence of cybersecurity and big data, providing insights into the ever-changing environment of cyber threats and solutions to protect these enormous databases. In the modern digital era, large amounts of data have evolved into the vital organs of businesses, providing the impetus for decision-making, creativity, and a competitive edge. Cyberattacks pose a persistent danger to this important resource since they can result in data breaches, financial losses, and harm to an organization's brand.
Features of particular interest include:
a. Understanding the Threat Landscape: The book gives a complete review of the many different types of cyber threats that target big data, including data breaches, ransomware attacks, and insider threats.
b. Analysing Real-World Case Studies: The purpose of this section is to provide the reader with useful insights into the strategies, methods, and processes that cyber adversaries use via the in-depth examination of real-world assaults on big data.
c. Strategies for Risk Mitigation: This book provides a comprehensive and actionable approach to reducing the dangers of cyberattacks on large amounts of data. It encompasses a broad variety of tactics, including network protection, encoding, and access restrictions
d. Emerging Technologies: This section introduces readers to cutting-edge technologies and best practices for securing large data.
e. Compliance with Regulations: The book examines the regulatory environment that governs data protection and privacy, including the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and industry-specific requirements.
f. Trends of the Future: This book covers current trends and issues in cybersecurity and gives forward-looking views into the future of big data security. This is done in recognition that the cybersecurity environment is always shifting and developing.
g. Contributors of Expertise: The book includes contributions from industry practitioners, data scientists, and cybersecurity specialists who have hands-on experience defending large data settings.

List of contents

1. Introduction .- 2.Internet of Things.- 3.Embedded IoT Security Framework.- 4.Defense Mechanism for Reactive Jamming Attack in IoT Networks.- 5.Statistical Results Validation.- 6.Conclusions and Future Directions.

About the author

Dr. Fadele Ayotunde Alaba is a distinguished academic and researcher with extensive experience in the field of Computer Science. Currently, he serves as a Full-Time Lecturer in the Department of Computer Science at the Federal College of Education, Zaria, Nigeria, a position he has held since 2010. His academic journey includes significant roles such as Research Assistant at the University of Malaya from 2017 to 2019 and Lecturer at the Federal Polytechnic Bauchi, Nigeria.

Dr. Alaba holds multiple advanced degrees in computer science, showcasing his dedication to continuous learning and expert development. He earned his second Ph.D. in Computer Science, specializing in Blockchain, from the International University of Bamenda (2020-2023) and his first Ph.D. in Computer Science from the University of Malaya (2016-2019). Additionally, he has a Master of Computer Science from Ahmadu Bello University, Zaria (2011-2014), and a Post Graduate Diploma in Education (PGDE) from Usman Danfodio University, Sokoto (2011-2012). He began his academic pursuit with a Bachelor of Computer Science (First Class Honours) from Nasarawa State University, Keffi (2004-2008).

Dr. Alaba's research interests are diverse and cutting-edge, including Big Data, Cloud Computing, the Internet of Things (IoT), Network Security, Blockchain, and Smart Contracts Security. His technical expertise encompasses working with Hadoop MapReduce, Contiki, and Cooja IoT Simulators, as well as data mining, machine learning, and statistical analysis software such as R, SPSS, SAS, and Weka.

He has contributed significantly to the academic community through his research publications in high-impact journals, with over 2000 citations. Notable among his works is the "Adaptive Stochastic Conjugate Gradient Optimization for Backpropagation Neural Networks," published in IEEE Access (2024) and various book chapters in Springer Nature Switzerland. His publications address crucial topics like AIoT-enabled smart grids, sustainable transportation through IoT, and smart contracts security.

Dr. Alaba has supervised numerous master's theses on subjects ranging from machine learning algorithms for intrusion detection in smart wearable devices to the impact of financial planning on the profitability of small-scale firms. He is also a frequent speaker at national and international conferences, sharing his insights on research and publication practices.

In recognition of his contributions, Dr. Alaba has received several awards, including the 2019 JNCA Best Survey Paper Award and the Best Presenter at the Annual Faculty of Computer Science and Information Technology Postgraduate Symposium in Malaysia (2017). He was also the Best Graduating Student from the Department of Mathematical Sciences, Computer Science Unit, Nasarawa State University, Keffi, in 2007/2008.

Dr. Alaba is serving as a consultant on the Editorial Board of Standardization at the TeTFund Center of Excellence for Technology Enhanced Learning since 2023. His commitment to academic excellence and innovative research continues to make significant impacts in the fields of computer science and technology.

Product details

• Authors: Fadele Ayotunde Alaba, Alvaro Rocha
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 25.08.2025
• EAN: 9783031659317
• ISBN: 978-3-0-3165931-7
• No. of pages: 113
• Illustrations: XVIII, 113 p. 43 illus., 31 illus. in color.
• Series: Studies in Systems, Decision and Control
• Subject: Natural sciences, medicine, IT, technology > Physics, astronomy > Miscellaneous