Frequently used machine learning algorithm for detecting the distributed denial-of-service (DDoS) attacks
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Richa Pandey
Abstract
Distributed denial-of-service (DDoS) attack is a type of denial-of-service (DOS) attack. Basically both are same in nature but DDoS attack in addition is related to distributed systems. In this attack, the attacker utilizes several distributed machines to bring the target system on a network on the knees. Nowadays Internet security is vulnerable as there is an increase in DDoS attacks in the Internet world. The DDoS attack is becoming very powerful with time so it is very important that DDoS attack must be detected first, and then the attack may be minimized. There are many methods that are introduced to defend DDoS attacks. In this chapter, we have studied and compared various frequently used machine learning algorithms that are helpful in detecting and combating the DDoS attacks.
Abstract
Distributed denial-of-service (DDoS) attack is a type of denial-of-service (DOS) attack. Basically both are same in nature but DDoS attack in addition is related to distributed systems. In this attack, the attacker utilizes several distributed machines to bring the target system on a network on the knees. Nowadays Internet security is vulnerable as there is an increase in DDoS attacks in the Internet world. The DDoS attack is becoming very powerful with time so it is very important that DDoS attack must be detected first, and then the attack may be minimized. There are many methods that are introduced to defend DDoS attacks. In this chapter, we have studied and compared various frequently used machine learning algorithms that are helpful in detecting and combating the DDoS attacks.
Kapitel in diesem Buch
- 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
Kapitel in diesem Buch
- 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
