Librería Portfolio

Features

Discusses DDoS attacks-their types, characteristics, causes, targets, and launching approaches
Includes an in-depth discussion on botnet technology and its evolution, trends, and challenges
Provides an adequate knowledge on various statistical, machine learning, knowledge-based, and soft computing methods and techniques for DDoS detection and prevention in networks and clouds
Covers a large number of DDoS reaction and tolerance mechanisms and their effectiveness in protecting network resources without compromising quality of service
Presents information on the practical use of a large number of tools and systems for launching DDoS attacks of various types and for monitoring their behaviors
Summary

DDoS Attacks: Evolution, Detection, Prevention, Reaction, and Tolerance discusses the evolution of distributed denial-of-service (DDoS) attacks, how to detect a DDoS attack when one is mounted, how to prevent such attacks from taking place, and how to react when a DDoS attack is in progress, with the goal of tolerating the attack. It introduces types and characteristics of DDoS attacks, reasons why such attacks are often successful, what aspects of the network infrastructure are usual targets, and methods used to launch attacks.

The book elaborates upon the emerging botnet technology, current trends in the evolution and use of botnet technology, its role in facilitating the launching of DDoS attacks, and challenges in countering the role of botnets in the proliferation of DDoS attacks. It introduces statistical and machine learning methods applied in the detection and prevention of DDoS attacks in order to provide a clear understanding of the state of the art. It presents DDoS reaction and tolerance mechanisms with a view to studying their effectiveness in protecting network resources without compromising the quality of services.

To practically understand how attackers plan and mount DDoS attacks, the authors discuss the development of a testbed that can be used to perform experiments such as attack launching, monitoring of network traffic, and detection of attacks, as well as for testing strategies for prevention, reaction, and mitigation. Finally, the authors address current issues and challenges that need to be overcome to provide even better defense against DDoS attacks.



Table of Contents

Introduction
Anomalies in Networks
Distributed Denial-of-Service (DDoS) Attacks
Causes of DDoS Attacks
Targets of DDoS Attacks
Launching of DDoS Attacks
Current Trends in Botnet Technology
Machine Learning in DDoS Attack Handling
DDoS Defense
Modules of a DDoS Defense System
Types of DDoS Defense Systems
DDoS Tools and Systems
DDoS Defense Evaluation
Prior Work
Contribution of This Book
Organization of This Book

DDoS Attacks, Machine Learning, and Measures
Issues in Internet Design
DDoS Attacks and Their Types
DDoS Attack Targets
Current Trends in DDoS Attacks
Strength of DDoS Attackers
Desired Characteristics of DDoS Defense System
Recent DDoS Attacks
Machine Learning Background
Some Empirical Studies
Chapter Summary

Botnets: Trends and Challenges
DDoS Attacks Using Stationary Botnets
Chapter Summary and Recommendations

DDoS Detection
Modules of a DDoS Defense Solution
Types of DDoS Defense Solutions
DDoS Detection Techniques
Chapter Summary

DDoS Prevention
DDoS Prevention Techniques
Chapter Summary

DDoS Reaction and Tolerance
Intrusion Response System (IRS)
DDoS Tolerance Approaches and Methods
Chapter Summary

Tools and Systems
Introduction
Types of Network Security Tools
Observations
TUCANNON+: DDoS Attack-Generation and Monitoring Tool
TUCannon Architecture
TUMonitor: Traffic Monitoring Module in TUCANNON+
DDoS Defense Systems
Chapter Summary

Research Challenges and Recommendations
Conclusion
Research Challenges