ABSTRACT

Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.

chapter 1|10 pages

Introduction

part I|4 pages

Supporting Technologies for BDMA and BDSP

chapter 2|12 pages

Data Security and Privacy

chapter 3|16 pages

Data Mining Techniques

chapter 4|8 pages

Data Mining for Security Applications

chapter 6|12 pages

Data Mining and Insider Threat Detection

part II|4 pages

Stream Data Analytics

chapter 8|12 pages

Challenges for Stream Data Classification

chapter 9|10 pages

Survey of Stream Data Classification

chapter 13|8 pages

Directions in Data Stream Classification

part III|4 pages

Stream Data Analytics for Insider Threat Detection

chapter 15|8 pages

Survey of Insider Threat and Stream Mining

chapter 16|6 pages

Ensemble-Based Insider Threat Detection

chapter 17|4 pages

Details of Learning Classes

chapter 18|10 pages

Experiments and Results for Nonsequence Data

chapter 19|10 pages

Insider Threat Detection for Sequence Data

chapter 20|10 pages

Experiments and Results for Sequence Data

chapter 21|14 pages

Scalability Using Big Data Technologies

part IV|4 pages

Experimental BDMA and BDSP Systems

chapter 27|16 pages

Big Data Analytics for Malware Detection

part V|4 pages

Next Steps for BDMA and BDSP

chapter 35|16 pages

Directions for BDSP and BDMA

chapter 36|8 pages

Summary and Directions