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
- 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.
Table of Contents
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
6. Cybersecurity Training
7. Machine Unlearning: Repairing Learning Models in Adversarial Environments
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
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.