1st Edition

Big Data Analytics in Cybersecurity

Edited By Onur Savas, Julia Deng Copyright 2017
    352 Pages 74 Color Illustrations
    by Auerbach Publications

    352 Pages 74 Color Illustrations
    by Auerbach Publications

    352 Pages 74 Color Illustrations
    by Auerbach Publications

    Big data is presenting challenges to cybersecurity. For an example, the Internet of Things (IoT) will reportedly soon generate a staggering 400 zettabytes (ZB) of data a year. Self-driving cars are predicted to churn out 4000 GB of data per hour of driving. Big data analytics, as an emerging analytical technology, offers the capability to collect, store, process, and visualize these vast amounts of data. Big Data Analytics in Cybersecurity examines security challenges surrounding big data and provides actionable insights that can be used to improve the current practices of network operators and administrators.

    Applying big data analytics in cybersecurity is critical. By exploiting data from the networks and computers, analysts can discover useful network information from data. Decision makers can make more informative decisions by using this analysis, including what actions need to be performed, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the network processes.

    Bringing together experts from academia, government laboratories, and industry, the book provides insight to both new and more experienced security professionals, as well as data analytics professionals who have varying levels of cybersecurity expertise. It covers a wide range of topics in cybersecurity, which include:

    • Network forensics

    • Threat analysis

    • Vulnerability assessment

    • Visualization

    • Cyber training.

    In addition, emerging security domains such as the IoT, cloud computing, fog computing, mobile computing, and cyber-social networks are examined.

    The book first focuses on how big data analytics can be used in different aspects of cybersecurity including network forensics, root-cause analysis, and security training. Next it discusses big data challenges and solutions in such emerging cybersecurity domains as fog computing, IoT, and mobile app security. The book concludes by presenting the tools and datasets for future cybersecurity research.

    I. Applying Big Data into Different Cybersecurity Aspects

    1. The Power of Big Data in Cybersecurity
    Song Luo, Malek Ben Salem, and Yan Zhai

    2. Big Data for Network Forensics
    Yi Cheng, Tung Thanh Nguyen, Hui Zeng, and Julia Deng

    3. Dynamic Analytics-Driven Assessment of Vulnerabilities and Exploitation
    Hasan Cam, Magnus Ljungberg, Akhilomen Oniha, and Alexia Schulz

    4. Root Cause Analysis for Cybersecurity
    Engin Kirda and Amin Kharraz

    5. Data Visualization for Cybersecurity
    Lane Harrison

    6. Cybersecurity Training
    Bob Pokorny

    7. Machine Unlearning: Repairing Learning Models in Adversarial Environments
    Yinzhi Cao

    II. Big Data in Emerging Cybersecurity Domains

    8. Big Data Analytics for Mobile App Security
    Doina Caragea and Xinming Ou

    9. Security, Privacy, and Trust in Cloud Computing
    Yuhong Liu, Ruiwen Li, Songjie Cai, and Yan (Lindsay) Sun

    10. Cybersecurity in Internet of Things (IoT)
    Wenlin Han and Yang Xiao

    11. Big Data Analytics for Security in Fog Computing
    Shanhe Yi and Qun Li

    12. Analyzing Deviant Socio-Technical Behaviors Using Social Network Analysis and Cyber Forensics-Based Methodologies
    Samer Al-Khateeb, Muhammad Hussain, and Nitin Agarwal

    III. Tools and Datasets for Cybersecurity

    13. Security Tools
    Matthew Matchen

    14. Data and Research Initiatives for Cybersecurity Analysis
    Julia Deng and Onur Savas



    Dr. Onur Savas is a data scientist at Intelligent Automation, Inc. (IAI), Rockville, MD. As a data scientist, he performs research and development (R&D), leads a team of data scientists, software engineers, and programmers, and contributes to IAI’s increasing portfolio of products. He has more than 10 years of R&D expertise in the areas of networks and security, social media, distributed algorithms, sensors, and statistics. His recent work focuses on all aspects of big data analytics and cloud computing with applications to network management, cybersecurity, and social networks. Dr. Savas has a PhD in electrical and computer engineering from Boston University, Boston, MA, and is the author of numerous publications in leading journals and conferences. At IAI, he has been the recipient of various R&D contracts from DARPA, ONR, ARL, AFRL, CTTSO, NASA, and other federal agencies. His work at IAI has contributed to the development and commercialization of IAI’s social media analytics tool Scraawl® (www.scraawl.com).

    Dr. Julia Deng is a principal scientist and Sr. Director of Network and Security Group at Intelligent Automation, Inc. (IAI), Rockville, MD. She leads a team of more than 40 scientists and engineers, and during her tenure at IAI, she has been instrumental in growing IAI’s research portfolio in networks and cybersecurity. In her role as a principal investigator and principal scientist, she initiated and directed numerous R&D programs in the areas of airborne networks, cybersecurity, network management, wireless networks, trusted computing, embedded system, cognitive radio networks, big data analytics, and cloud computing. Dr. Deng has a PhD from the University of Cincinnati, Cincinnati, OH, and has published over 30 papers in leading international journals and conference proceedings.