
Traffic Anomaly Detection
Description
Key Features
- A new information-theory based technique for traffic anomaly detection (typical day analysis)
- Introductory chapters to anomaly detection methods including control charts, tests of goodness-of-fit Mutual Information
- Contains comparative analysis of traffic anomaly detection methods
Readership
Scientific and Engineering communities working on Anomaly detection in the context of Network Security. In particular, early researchers, post-docs and engineers with an interest in this field
Table of Contents
1: Introduction to Traffic Anomaly Detection Methods
- Abstract
- 1.1 Cumulative sum control charts (CUSUM)
- 1.2 Tests of goodness-of-fit
- 1.3 Mutual information (MI)
2: Finding the Optimal Aggregation Period
- Abstract
- 2.1 Introduction
- 2.2 State of the art
- 2.3 Macroscopic observation of traffic
- 2.4 Average-day analysis
- 2.5 Conclusion
3: Comparative Analysis of Traffic Anomaly Detection Methods
- Abstract
- 3.1 Introduction
- 3.2 State of the art
- 3.3 Average-day preliminary analysis
- 3.4 Proposed change point detection algorithms
- 3.5 Behavior of the analyzed algorithms
- 3.6 Conclusion
4: Proposal of a New Information-theory Technique
- Abstract
- 4.1 Introduction
- 4.2 Related work
- 4.3 Analysis of traffic anomaly detection methods applied to typical day profile
- 4.4 Conclusions
- 4.5 Acknowledgments
Product details
- No. of pages: 70
- Language: English
- Copyright: © ISTE Press - Elsevier 2015
- Published: October 30, 2015
- Imprint: ISTE Press - Elsevier
- Hardcover ISBN: 9781785480126
- eBook ISBN: 9780081008072
About the Authors
Antonio Cuadra-Sánchez
He currently leads the Celtic NOTTS projectand co-leads the Customer Experience Management (CEM) Implementation Guide at the TeleManagement Forum.