Acknowledgments
by Latifur Khan, Mohammad Mehedy Masud, Pallabi Parveen, Bhavani Thuraisingham
Big Data Analytics with Applications in Insider Threat Detection
- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- Contents
- Preface
- Acknowledgments
- Permissions
- Authors
- Chapter 1: Introduction
- Part I: Supporting Technologies for BDMA and BDSP
- Introduction to Part I
- Chapter 2: Data Security and Privacy
- Chapter 3: Data Mining Techniques
- Chapter 4: Data Mining for Security Applications
- Chapter 5: Cloud Computing and Semantic Web Technologies
- 5.1 Introduction
- 5.2 Cloud Computing
- 5.3 Semantic Web
- 5.4 Semantic Web and Security
- 5.5 Cloud Computing Frameworks Based on Semantic Web Technologies
- 5.6 Summary and Directions
- References
- Chapter 6: Data Mining and Insider Threat Detection
- 6.1 Introduction
- 6.2 Insider Threat Detection
- 6.3 The Challenges, Related Work, and Our Approach
- 6.4 Data Mining for Insider Threat Detection
- 6.5 Comprehensive Framework
- 6.6 Summary and Directions
- References
- Chapter 7: Big Data Management and Analytics Technologies
- Conclusion to Part I
- Part II: Stream Data Analytics
- Introduction to Part II
- Chapter 8: Challenges for Stream Data Classification
- Chapter 9: Survey of Stream Data Classification
- Chapter 10: A Multi-Partition, Multi-Chunk Ensemble for Classifying Concept-Drifting Data Streams
- Chapter 11: Classification and Novel Class Detection in Concept-Drifting Data Streams
- Chapter 12: Data Stream Classification with Limited Labeled Training Data
- Chapter 13: Directions in Data Stream Classification
- Conclusion to Part II
- Part III: Stream Data Analytics for Insider Threat Detection
- Introduction to Part III
- Chapter 14: Insider Threat Detection as a Stream Mining Problem
- Chapter 15: Survey of Insider Threat and Stream Mining
- Chapter 16 Ensemble-Based Insider Threat Detection
- Chapter 17: Details of Learning Classes
- Chapter 18: Experiments and Results for Nonsequence Data
- Chapter 19: Insider Threat Detection for Sequence Data
- Chapter 20: Experiments and Results for Sequence Data
- Chapter 21: Scalability Using Big Data Technologies
- Chapter 22: Stream Mining and Big Data for Insider Threat Detection
- Conclusion to Part III
- Part IV: Experimental BDMA and BDSP Systems
- Introduction to Part IV
- Chapter 23: Cloud Query Processing System for Big Data Management
- Chapter 24: Big Data Analytics for Multipurpose Social Media Applications
- 24.1 Introduction
- 24.2 Our Premise
- 24.3 Modules of Inxite
- 24.4 Other Applications
- 24.5 Related Work
- 24.6 Summary and Directions
- References
- Chapter 25: Big Data Management and Cloud for Assured Information Sharing
- Chapter 26: Big Data Management for Secure Information Integration
- Chapter 27: Big Data Analytics for Malware Detection
- Chapter 28: A Semantic Web-Based Inference Controller for Provenance Big Data
- Conclusion to Part IV
- Part V: Next Steps for BDMA and BDSP
- Introduction to Part V
- Chapter 29: Confidentiality, Privacy, and Trust for Big Data Systems
- 29.1 Introduction
- 29.2 Trust, Privacy, and Confidentiality
- 29.3 CPT Framework
- 29.4 Our Approach to Confidentiality Management
- 29.5 Privacy for Social Media Systems
- 29.6 Trust for Social Networks
- 29.7 Integrated System
- 29.8 CPT within the Context of Big Data and Social Networks
- 29.9 Summary and Directions
- References
- Chapter 30: Unified Framework for Secure Big Data Management and Analytics
- Chapter 31: Big Data, Security, and the Internet of Things
- Chapter 32: Big Data Analytics for Malware Detection in Smartphones
- 32.1 Introduction
- 32.2 Our Approach
- 32.3 Our Experimental Activities
- 32.4 Infrastructure Development
- 32.4.1 Virtual Laboratory Development
- 32.4.1.1 Laboratory Setup
- 32.4.1.2 Programming Projects to Support the Virtual Lab
- 32.4.1.3 An Intelligent Fuzzier for the Automatic Android GUI Application Testing
- 32.4.1.4 Problem Statement
- 32.4.1.5 Understanding the Interface
- 32.4.1.6 Generating Input Events
- 32.4.1.7 Mitigating Data Leakage in Mobile Apps Using a Transactional Approach
- 32.4.1.8 Technical Challenges
- 32.4.1.9 Experimental System
- 32.4.1.10 Policy Engine
- 32.4.2 Curriculum Development
- 32.4.1 Virtual Laboratory Development
- 32.5 Summary and Directions
- References
- Chapter 33: Toward a Case Study in Healthcare for Big Data Analytics and Security
- 33.1 Introduction
- 33.2 Motivation
- 33.3 Methodologies
- 33.4 The Framework Design
- 33.5 Summary and Directions
- References
- Chapter 34: Toward an Experimental Infrastructure and Education Program for BDMA and BDSP
- Chapter 35: Directions for BDSP and BDMA
- 35.1 Introduction
- 35.2 Issues in BDSP
- 35.3 Summary of Workshop Presentations
- 35.3.1 Keynote Presentations
- 35.3.1.1 Toward Privacy Aware Big Data Analytics
- 35.3.1.2 Formal Methods for Preserving Privacy While Loading Big Data
- 35.3.1.3 Authenticity of Digital Images in Social Media
- 35.3.1.4 Business Intelligence Meets Big Data: An Overview of Security and Privacy
- 35.3.1.5 Toward Risk-Aware Policy-Based Framework for BDSP
- 35.3.1.6 Big Data Analytics: Privacy Protection Using Semantic Web Technologies
- 35.3.1.7 Securing Big Data in the Cloud: Toward a More Focused and Data-Driven Approach
- 35.3.1.8 Privacy in a World of Mobile Devices
- 35.3.1.9 Access Control and Privacy Policy Challenges in Big Data
- 35.3.1.10 Timely Health Indicators Using Remote Sensing and Innovation for the Validity of the Environment
- 35.3.1.11 Additional Presentations
- 35.3.1.12 Final Thoughts on the Presentations
- 35.3.1 Keynote Presentations
- 35.4 Summary of the Workshop Discussions
- 35.5 Summary and Directions
- References
- Conclusion to Part V
- Chapter 36: Summary and Directions
- Appendix A: Data Management Systems: Developments and Trends
- Appendix B: Database Management Systems
- Index
We thank the administration at the Erik Jonsson School of Engineering and Computer Science at The University of Texas at Dallas for giving us the opportunity to conduct our research. We also thank Ms. Rhonda Walls, our project coordinator, for proofreading and editing the chapters. Without her hard work this book would not have been possible. We thank many additional people who have supported our work or collaborated with us.
•Dr. Robert Herklotz (retired) from the Air Force Office of Scientific Research for funding our research on insider threat detection as well as several of our experimental systems.
•Dr. Victor Piotrowski from the National Science Foundation for funding our capacity building work on assured cloud computing and secure mobile computing.
•Dr. Ashok Agrawal, formerly of National Aeronautics and Space Administration, for funding our research on stream data mining.
•Professor Jiawei Han and his team from the University of Illinois at Urbana Champaign as well as Dr. Charu Agrawal from IBM Research for collaborating with us on stream data mining.
•Our colleagues Dr. Murat Kantarcioglu, Dr. Kevin Hamlen, Dr. Zhiqiang Lin, Dr. Kamil Sarac and Dr. Alvaro Cardenas at The University of Texas at Dallas for discussions on our work.
•Our collaborators on Assured Information Sharing at Kings College, University of London (Dr. Maribel Fernandez and the late Dr. Steve Barker), the University of Insubria, Italy (Dr. Elena Ferrari and Dr. Barbara Carminati), Purdue University (Dr. Elisa Bertino), and the University of Maryland, Baltimore County (Dr. Tim Finin and Dr. Anupam Joshi).
•The following people for their technical contributions: Dr. Murat Kantarciogu for his contributions to Chapters 25, 26, 28, 31, and 34; Mr Ramkumar Paranthaman from Amazon for his contributions to Chapter 7; Dr. Tyrone Cadenhead from Blue Cross Blue Shield for his contributions to Chapter 28 (part of his PhD thesis); Dr. Farhan Husain and Dr. Arindam Khaled, both from Amazon, for their contributions to Chapter 23 (part of Husain’s PhD thesis); Dr. Satyen Abrol, Dr. Vaibhav Khadilkar, and Mr Gunasekar Rajasekar for their contributions to Chapter 24; Dr. Vaibhav Khadilkar and Dr. Jyothsna Rachapalli for their contributions to Chapter 25; Mr Pranav Parikh from Yahoo for his contributions to Chapter 26 (part of his MS thesis); Dr. David Lary and Dr. Vibhav Gogate, both from The University of Texas at Dallas, for their contributions to Chapter 33; Dr. Alvaro Cardenas for his contributions to Chapter 31; Dr. Zhiqiang Lin for his contributions to Chapters 32 and 34.
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