Secure Data Science: Integrating Cyber Security and Data Science

Secure data science is a critical area in both cyber security and data science, as it combines the techniques of data science with cyber security to solve problems such as intrusion detection, malware analysis, and insider threat detection. This book provides a comprehensive overview of integrating both cyber security and data science, discussing both theory and practice in secure data science. It also covers emerging trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. The book is a useful resource for researchers, software developers, educators, and managers who want to understand the high-level concepts and technical details of designing and implementing secure data science-based systems.

Format: Hardback
Length: 436 pages
Publication date: 18 April 2022
Publisher: Taylor & Francis Ltd

Secure data science, a rapidly emerging field that seamlessly combines cyber security and data science, holds immense importance in both domains. The advent of novel data science techniques has opened up exciting possibilities for addressing pressing cyber security challenges, such as intrusion detection, malware analysis, and insider threat detection. These techniques have broad applications beyond cyber security, spanning healthcare, finance, manufacturing, and marketing. However, the same data science techniques that enhance security can also be exploited by malware, posing significant risks to these diverse industries.

One of the most concerning aspects of secure data science is the ability to infer highly private and sensitive information from public data. This poses a significant threat to individual privacy, as unauthorized individuals can exploit this information for malicious purposes. This book is a groundbreaking resource that provides a comprehensive overview of integrating cyber security and data science. It delves into both theoretical principles and practical applications, offering a comprehensive understanding of secure data science.

The book begins by providing an overview of security and privacy for big data services and cloud computing, essential foundations for secure data science. It then explores the applications of data science in cyber security, including malware analysis and insider threat detection. The book also addresses emerging trends in adversarial machine learning, which are used to develop malicious algorithms that can evade detection.

To mitigate these threats, the book offers solutions to protect data science techniques from malicious attacks. It discusses some emerging trends in trustworthy analytics, ensuring that analytics algorithms are robust against malicious interference. Additionally, the book focuses on the privacy concerns associated with the collection of massive amounts of data and explores potential solutions to safeguard individual privacy.

The book concludes by discussing the integration of services computing, including cloud-based services for secure data science. It highlights the importance of integrating various services and technologies to enhance security and privacy. Overall, this book is a valuable resource for professionals, researchers, and students interested in developing secure data science solutions. It provides a comprehensive overview of integrating cyber security and data science, covering theoretical principles, practical applications, and emerging trends. By addressing the challenges posed by malware and privacy threats, this book empowers individuals and organizations to build resilient and trustworthy systems that can withstand the ever-evolving threat landscape.

Weight: 1000g
Dimension: 254 x 178 (mm)
ISBN-13: 9780367534103