Librería Portfolio

Features
Uses big data to analyze and detect threats, as well as identify vulnerablities
Presents practical analytical tools to monitor and manage network security
Covers analytics applications for securing cloud and internet of things environments
Written by experts in academia, industry, and government
Includes case study that shows practical applications of big data analytics
Summary
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, o?ers 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 ?rst focuses on how big data analytics can be used in di?erent 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
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

Index