Selected practical and effective techniques to combat distributed denial-of-service (DDoS) attacks
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Rajeev Singh
Abstract
Distributed denial-of-service (DDoS) attacks cause devastating effects on the web services and hence harms the digital availability. The DDoS attackers use vulnerabilities exposed by new networking technologies such as wireless, mobile, IoT, and associated protocol weaknesses for bringing down the networks and servers. Owing to availability of easily available tools and botnet armies, the DDoS attack incidences in the Internet world are increasing day by day. Though several techniques have been proposed by the researchers against DDoS attacks, this chapter presents selected practical and effective DDoS techniques in use these days. These includes: content distribution networks (CDNs), scrubbing centers, blockchain technology, software define networking (SDN), and honeypots. The chapter discusses these methodologies and tries to figure out the reasons for success of these methodologies against DDoS attacks. An analysis of these solution and comparative study is done so that future proposals against DDoS attacks based upon principles of these methodologies may develop.
Abstract
Distributed denial-of-service (DDoS) attacks cause devastating effects on the web services and hence harms the digital availability. The DDoS attackers use vulnerabilities exposed by new networking technologies such as wireless, mobile, IoT, and associated protocol weaknesses for bringing down the networks and servers. Owing to availability of easily available tools and botnet armies, the DDoS attack incidences in the Internet world are increasing day by day. Though several techniques have been proposed by the researchers against DDoS attacks, this chapter presents selected practical and effective DDoS techniques in use these days. These includes: content distribution networks (CDNs), scrubbing centers, blockchain technology, software define networking (SDN), and honeypots. The chapter discusses these methodologies and tries to figure out the reasons for success of these methodologies against DDoS attacks. An analysis of these solution and comparative study is done so that future proposals against DDoS attacks based upon principles of these methodologies may develop.
Chapters in this book
- Frontmatter I
- Preface V
- Acknowledgments VII
- About the Editors IX
- Contents XI
- List of contributors XIII
- Impact evaluation of DDoS and Malware attack using IoT devices 1
- Understanding and implementation of machine learning using support vector machine for efficient DDoS attack detection 29
- Cryptographic method based on Catalan objects and enumerative chess problem 51
- Distributed denial-of-service attacks and mitigation in wireless sensor networks 67
- New techniques for DDoS attacks mitigation in resource-constrained networks 83
- Detection and behavioral analysis of botnets using honeynets and classification techniques 131
- Selected practical and effective techniques to combat distributed denial-of-service (DDoS) attacks 159
- Probability, queuing, and statistical perspective in the distributed denial-of-service attacks domain 173
- Frequently used machine learning algorithm for detecting the distributed denial-of-service (DDoS) attacks 189
- Utilization of puzzles for protection against DDoS attacks 203
- Index 217
Chapters in this book
- Frontmatter I
- Preface V
- Acknowledgments VII
- About the Editors IX
- Contents XI
- List of contributors XIII
- Impact evaluation of DDoS and Malware attack using IoT devices 1
- Understanding and implementation of machine learning using support vector machine for efficient DDoS attack detection 29
- Cryptographic method based on Catalan objects and enumerative chess problem 51
- Distributed denial-of-service attacks and mitigation in wireless sensor networks 67
- New techniques for DDoS attacks mitigation in resource-constrained networks 83
- Detection and behavioral analysis of botnets using honeynets and classification techniques 131
- Selected practical and effective techniques to combat distributed denial-of-service (DDoS) attacks 159
- Probability, queuing, and statistical perspective in the distributed denial-of-service attacks domain 173
- Frequently used machine learning algorithm for detecting the distributed denial-of-service (DDoS) attacks 189
- Utilization of puzzles for protection against DDoS attacks 203
- Index 217
