Enhancing Computer Security with Smart Technology / Edition 1

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Overview

Divided into two major parts, Enhancing Computer Security with Smart Technology introduces the problems of computer security to researchers with a machine learning background, then introduces machine learning concepts to computer security professionals.

Realizing the massive scope of these subjects, the author concentrates on problems related to the detection of intrusions through the application of machine learning methods and on the practical algorithmic aspects of machine learning and its role in security.

A collection of tutorials that draw from a broad spectrum of viewpoints and experience, this volume is made up of chapters written by specialists in each subject field. It is accessible to any professional with a basic background in computer science. Following an introduction to the issue of cyber-security and cyber-trust, the book offers a broad survey of the state-of-the-art in firewall technology and of the importance of Web application security. The remainder of the book focuses on the use of machine learning methods and tools and their performance.

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Product Details

  • ISBN-13: 9780849330452
  • Publisher: Taylor & Francis
  • Publication date: 11/21/2005
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 288
  • Product dimensions: 6.20 (w) x 9.10 (h) x 0.80 (d)

Table of Contents

Cyber-Security and Cyber-Trust
V Rao Vemuri
Introduction
Cyber-Security
Cyber-Trust
What the Future Holds

Network Firewalls
Kenneth Ingham and Stephanie Forrest
Abstract
Introduction
The Need for Firewalls
Firewall Architectures
Firewalls at Various ISO Network Layers
Other Approaches
Firewall Testing
What Firewalls Do Not Protect Against
Future Challenges for Firewalls
Conclusion
References

Web Application Security: The Next Battleground
Abhishek Kumar, Roshen Chandran, and Vinod Vasudevan
Threats to Web Applications
Vulnerabilities in Web Applications
Attack Techniques
Preventing Vulnerabilities in Web Applications
Conclusion
Notes
References

Relevance of Machine Learning
V Rao Vemuri
Introduction
Place of Intrusion Detection in the Security Landscape
Machine Learning beyond Intrusion Detection
Machine Learning and Computational Learning Theory
Some Popular Machine Learning Methods
Making Machine Learning More Useful
Summary
References

Machine Learning in Intrusion Detection
Yihua Liao and V Rao Vemuri
Introduction
Intrusion Detection
Machine Learning Approaches to Anomaly Detection
Audit Data.
Issues in Anomaly Detection
Open Questions and Future Directions
Illustrative Example: Adaptive Anomaly Detection
Summary
References

Cyber-Security Challenges: Designing Efficient
Intrusion Detection Systems and Anti-Virus Tools
Srinivas Mukkamala, Andrew Sung, and Ajith Abraham
Introduction to IDSs
A Review on IDSs
Computer Attack Taxonomy
Significant Feature Selection for Intrusion Detection
Detection of Probes and DoS Attacks
Attacks on IDSs
Attacks on Anti-Virus Tools
Conclusions
Acknowledgments
References

Artificial Immune Systems in Intrusion Detection
Dipankar Dasgupta and Fabio Gonzalez
Introduction
Artificial Immune Systems
RNS
Intrusion Detection Problem
Experimentation
Summary
Bibliography

Application of Wavelets in Network Security
Challa S. Sastry and Sanjay Rawat
Introduction
A Brief Introduction to Self-Similarity
A Brief Introduction to Wavelet Analysis
Application of Wavelets
Wavelets for HIDS
Wavelets for Network-Based IDS
Simulation Results
An Observation for Future Work and Conclusion
Appendix
Acknowledgment
References

Application of Exploratory Multivariate Analysis
for Network Security
Khaled Labib and V Rao Vemuri
Abstract
Introduction
The Intrusion Detection Problem
The S Language and Its Environment
Introduction to Multivariate Analysis Methods
DoS and Network Probe Attacks
Data Collection and Preprocessing
Results
Acknowledgment
References
Index

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