Secure Data Science
Integrating Cyber Security and Data Science
- Available for pre-order. Item will ship after April 18, 2022
Secure Data Science, that integrates Cyber Security and Data Science, is becoming one of the critical areas both in Cyber Security and Data Science. This is because the novel data science techniques being developed have applications in solving cyber security problems such as Intrusion Detection, Malware Analysis and Insider Threat Detection. However, the data science techniques being applied not only for cyber security but for every application area including Healthcare, Finance, Manufacturing and Marketing could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data which could result in the violation of individual privacy. This book is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in Secure Data Science.
After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. In particular data science for applications such as malware analysis and insider threat detection are discussed. Then it discusses trends in areas such as adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media.
This book is a useful resource for researchers, software developers, educators and managers who want to understand both the high level concepts as well as the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in Secure Data Science. Furthermore, the book provides numerous references that would be helpful for the reader to get more details about Secure Data Science.
Table of Contents
Part I: Supporting Technologies
2. Data Security and Privacy
3. Data Mining and Security
4. Big Data, Cloud, Semantic Web and Social Network Technologies
5. Big Data Analytics, Security and Privacy
Part II: Data Science for Cyber Security
6. Data Science for Malicious Executables
7. Stream Analytics for Malware Detection
8. Data Science for Insider Threat Detection
9. Stream Analytics for Insider Threat Detection
Part III: Security and Privacy Enhanced Data Science
10. Adversarial Support Vector Machine Learning
11. Adversarial Learning Using Relevance Vector Machine Ensembles
12. Privacy Preserving Decision Trees
13. Towards a Privacy-aware Policy-based Quantified Self Data Management Framework
14. Data Science, COVID-19 Pandemic, Privacy and Civil Liberties
Part IV: Access Control and Data Science
15. Secure Cloud Query Processing Based on Access Control for Big Data Systems
16. Access Control-based Assured Information Sharing in the Cloud
17. Access Control for Social Network Data Management
18. Inference and Access Control for Big Data
19. Emerging Applications for Secure Data Science: Internet of Transportation Systems
20. Summary and Directions
Dr. Bhavani Thuraisingham is the Louis A. Beecherl, Jr. Distinguished Professor of Computer Science and the Executive Director of the Cyber Security Research and Education Institute (CSI) at the University of Texas at Dallas.Dr. Latifur R. Khan is currently an Associate Professor in computer science at at the University of Texas at Dallas.Dr. Murat Kantarcioglu is Professor of Computer Science and Director of the University of Texas at Dallas Data Security and Privacy Lab. His research focuses on creating technologies that can efficiently extract useful information from any data without sacrificing privacy or security. Recently, he has been working on security and privacy issues raised by data mining, privacy issues in social networks, security issues in databases, privacy issues in health care, applied cryptography for data security, risk and incentive issues in assured information sharing, use of data mining for fraud detection, botnet detection and homeland security.