This comprehensive book explores the challenges posed by cyberattacks on big data systems and their corresponding mitigation strategies. The book is organized into logical chapters, each focusing on specific aspects of the subject, ensuring clarity and depth in addressing the multifaceted nature of the problem. The introductory chapter provides a clear overview of the problem, introducing the prevalence of cyberattacks on big data systems, the motivation for addressing these risks, and the goals of the book. It also outlines the goals of the book, such as identifying vulnerabilities, evaluating mitigation strategies, and proposing integrated solutions. The second chapter provides a detailed examination of cyberattacks, emphasizing their implications for big data systems. It systematically categorizes tools and techniques available for mitigating these risks, including identity and access management (IAM), symmetric data encryption, network firewalls, IDPS, data loss prevention (DLP), SIEM, DDoS protection, and big data backup and recovery strategies.
The book focuses on key mitigation techniques, such as IAM, encryption methods, network segmentation, firewalls, and intrusion detection systems. It also proposes an integrated cybersecurity model, combining these solutions for enhanced effectiveness against cyberattacks. The book also identifies research gaps and suggests areas for future research, such as adapting to emerging technologies and improving scalability in big data security frameworks. The book is a valuable resource for cybersecurity professionals, researchers, and practitioners aiming to address the unique challenges posed by cyberattacks on big data systems.
The book aims to equip various professionals with the knowledge and strategies necessary to address the vulnerabilities associated with cyberattacks on big data environments.
Series
Language
Place of publication
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
12 farbige Abbildungen
IX, 111 p. 12 illus., 11 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 13 mm
Weight
ISBN-13
978-3-031-88569-3 (9783031885693)
DOI
10.1007/978-3-031-88570-9
Schweitzer Classification
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.
