Handbook of Big Data Privacy

1. Front Matter

   Pages i-ix

   PDF

2. Big Data and Privacy: Challenges and Opportunities

   • Amin Azmoodeh, Ali Dehghantanha

   Pages 1-5

3. AI and Security of Critical Infrastructure

   • Jacob Sakhnini, Hadis Karimipour, Ali Dehghantanha, Reza M. Parizi

   Pages 7-36

4. Industrial Big Data Analytics: Challenges and Opportunities

   • Abdulrahman Al-Abassi, Hadis Karimipour, Hamed HaddadPajouh, Ali Dehghantanha, Reza M. Parizi

   Pages 37-61

5. A Privacy Protection Key Agreement Protocol Based on ECC for Smart Grid

   • Mostafa Farhdi Moghadam, Amirhossein Mohajerzdeh, Hadis Karimipour, Hamid Chitsaz, Roya Karimi, Behzad Molavi

   Pages 63-76

6. Applications of Big Data Analytics and Machine Learning in the Internet of Things

   • Shamim Yousefi, Farnaz Derakhshan, Hadis Karimipour

   Pages 77-108

7. A Comparison of State-of-the-Art Machine Learning Models for OpCode-Based IoT Malware Detection

   • William Peters, Ali Dehghantanha, Reza M. Parizi, Gautam Srivastava

   Pages 109-120

8. Artificial Intelligence and Security of Industrial Control Systems

   • Suby Singh, Hadis Karimipour, Hamed HaddadPajouh, Ali Dehghantanha

   Pages 121-164

9. Enhancing Network Security Via Machine Learning: Opportunities and Challenges

   • Mahdi Amrollahi, Shahrzad Hadayeghparast, Hadis Karimipour, Farnaz Derakhshan, Gautam Srivastava

   Pages 165-189

10. Network Security and Privacy Evaluation Scheme for Cyber Physical Systems (CPS)

    • Mridula Sharma, Haytham Elmiligi, Fayez Gebali

    Pages 191-217

11. Anomaly Detection in Cyber-Physical Systems Using Machine Learning

    • Hossein Mohammadi Rouzbahani, Hadis Karimipour, Abolfazl Rahimnejad, Ali Dehghantanha, Gautam Srivastava

    Pages 219-235

12. Big Data Application for Security of Renewable Energy Resources

    • Hossein Mohammadi Rouzbahani, Hadis Karimipour, Gautam Srivastava

    Pages 237-254

13. Big-Data and Cyber-Physical Systems in Healthcare: Challenges and Opportunities

    • Jesus Castillo Cabello, Hadis Karimipour, Amir Namavar Jahromi, Ali Dehghantanha, Reza M. Parizi

    Pages 255-283

14. Privacy Preserving Abnormality Detection: A Deep Learning Approach

    • Wenyu Han, Amin Azmoodeh, Hadis Karimipour, Simon Yang

    Pages 285-303

15. Privacy and Security in Smart and Precision Farming: A Bibliometric Analysis

    • Sanaz Nakhodchi, Ali Dehghantanha, Hadis Karimipour

    Pages 305-318

16. A Survey on Application of Big Data in Fin Tech Banking Security and Privacy

    • Mahdi Amrollahi, Ali Dehghantanha, Reza M. Parizi

    Pages 319-342

17. A Hybrid Deep Generative Local Metric Learning Method for Intrusion Detection

    • Mahdis Saharkhizan, Amin Azmoodeh, Hamed HaddadPajouh, Ali Dehghantanha, Reza M. Parizi, Gautam Srivastava

    Pages 343-357

18. Malware Elimination Impact on Dynamic Analysis: An Experimental Machine Learning Approach

    • Mohammad Nassiri, Hamed HaddadPajouh, Ali Dehghantanha, Hadis Karimipour, Reza M. Parizi, Gautam Srivastava

    Pages 359-370

19. RAT Hunter: Building Robust Models for Detecting Remote Access Trojans Based on Optimum Hybrid Features

    • Mohammad Mehdi BehradFar, Hamed HaddadPajouh, Ali Dehghantanha, Amin Azmoodeh, Hadis Karimipour, Reza M. Parizi et al.

    Pages 371-383

20. Active Spectral Botnet Detection Based on Eigenvalue Weighting

    • Amin Azmoodeh, Ali Dehghantanha, Reza M. Parizi, Sattar Hashemi, Bahram Gharabaghi, Gautam Srivastava

    Pages 385-397

This handbook provides comprehensive knowledge and includes an overview of the current state-of-the-art of Big Data Privacy, with chapters written by international world leaders from academia and industry working in this field. The first part of this book offers a review of security challenges in critical infrastructure and offers methods that utilize acritical intelligence (AI) techniques to overcome those issues. It then focuses on big data security and privacy issues in relation to developments in the Industry 4.0. Internet of Things (IoT) devices are becoming a major source of security and privacy concern in big data platforms. Multiple solutions that leverage machine learning for addressing security and privacy issues in IoT environments are also discussed this handbook. 


The second part of this handbook is focused on privacy and security issues in different layers of big data systems. It discusses about methods for evaluating security and privacy of big data systems on network, application and physical layers. This handbook elaborates on existing methods to use data analytic and AI techniques at different layers of big data platforms to identify privacy and security attacks.  The final part of this handbook is focused on analyzing cyber threats applicable to the big data environments. It offers an in-depth review of attacks applicable to big data platforms in smart grids, smart farming, FinTech, and health sectors. Multiple solutions are presented to detect, prevent and analyze cyber-attacks and assess the impact of malicious payloads to those environments.  


This handbook provides information for security and privacy experts in most areas of big data including; FinTech, Industry 4.0, Internet of Things, Smart Grids, Smart Farming and more. Experts working in big data, privacy, security, forensics, malware analysis, machine learning and data analysts will find this handbook useful as a reference. Researchers and advanced-level computer science students focused on computer systems, Internet of Things, Smart Grid, Smart Farming, Industry 4.0 and network analysts will also find this handbook useful as a reference.

• cyber threat
• cyber security
• privacy
• big data
• threat intelligence
• machine learning
• cyber forensics
• intrusion detection
• incident response
• cyber defense
• malware campaign detection
• indicators of compromise
• evidence correlation
• data security

• University of Texas at San Antonio, San Antonio, USA

  Kim-Kwang Raymond Choo

• Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, Canada

  Ali Dehghantanha

​Kim-Kwang Raymond Choo holds the Cloud Technology Endowed Professorship at The University of Texas at San Antonio (UTSA), San Antonio, TX, USA. In 2015 he and his team won the Digital Forensics Research Challenge organized by Germany's University of Erlangen-Nuremberg. He is the recipient of the 2019 IEEE TCSC Award for Excellence in Scalable Computing (Middle Career Researcher), 2018 UTSA College of Business Col. Jean Piccione and Lt. Col. Philip Piccione Endowed Research Award for Tenured Faculty, British Computer Society's 2019 Wilkes Award Runner-up, 2019 EURASIP JWCN Best Paper Award, Korea Information Processing Society's JIPS Survey Paper Award (Gold) 2019, IEEE Blockchain 2019 Outstanding Paper Award, Best Paper Awards from IEEE TrustCom 2018 and ESORICS 2015, Fulbright Scholarship in 2009, 2008 Australia Day Achievement Medallion, and British Computer Society's Wilkes Award in 2008. He is also a Fellow of the Australian Computer Society, an IEEE Senior Member, and Co-Chair of IEEE Multimedia Communications Technical Committee's Digital Rights Management for Multimedia Interest Group.

Ali Dehghantanha is the director of Cyber Science Lab in the University of Guelph, Ontario, Canada. His lab is focused on building AI-powered solutions to support cyber threat attribution, cyber threat hunting and digital forensics tasks in Internet of Things (IoT), Industrial IoT, and Internet of Military of Things (IoMT) environments. Ali has served for more than a decade in a variety of industrial and academic positions with leading players in cyber security and AI. Prior to joining UofG, he has served as a Sr. Lecturer in the University of Sheffield - UK. He is an EU Marie-Curie Fellow alumnus and an IEEE Sr. member. He received his Ph.D. in Security in Computing in 2011 and his M.Sc. in Security in Computing in 2008.

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