Flow-Based Detection and Mitigation of Low-Rate DDOS Attack in SDN Environment Using Machine Learning Techniques

2021 ◽  
pp. 193-205
Author(s):  
K. Muthamil Sudar ◽  
P. Deepalakshmi
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Ddos Attack ◽  
Learning Techniques ◽  
Low Rate

A New Framework for Classification of Distributed Denial of Service (DDOS) Attack in Cloud Computing by Machine Learning Techniques

2014 ◽  
Vol 20 (1) ◽  
pp. 175-178
Author(s):  
Mostafa Behzadi ◽  
Ramlan Mahmod ◽  
Mehdi Barati ◽  
Azizol Bin Hj Abdullah ◽  
Mahda Noura
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Learning Techniques ◽  
New Framework

scholarly journals Detection of DDoS Attack in TCP protocol using Hybrid Machine Learning Techniques

2020 ◽  
pp. 253-258
Author(s):  
Vinod Desai ◽  
Aravind Pradhani ◽  
Sheetal Majukar
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Fast Detection ◽  
Ddos Attack ◽  
Tcp Protocol ◽  
Learning Techniques ◽  
Flash Crowd ◽  
Attack Scenario ◽  
Learning Technique ◽  
Network Status

Recently, damage caused by DDoS attacks increases year by year. Along with the advancement of communication technology, this kind of attack also evolves and it has become more complicated and hard to detect using flash crowd agent, slow rate attack and also amplification attack that exploits a vulnerability in DNS server. Fast detection of the DDoS attack, quick response mechanisms and proper mitigation are a must for an organization. An investigation has been performed on DDoS attack and it analyzes the details of its phase using machine learning technique to classify the network status. In this paper, we propose a hybrid KNN-SVM method on classifying, detecting and predicting the DDoS attack. The simulation result showed that each phase of the attack scenario is partitioned well and we can detect precursors of DDoS attack as well as the attack itself.

DDoS Attacks and Defense Mechanisms Using Machine Learning Techniques for SDN

2021 ◽  
pp. 248-264
Author(s):  
Rochak Swami ◽  
Mayank Dave ◽  
Virender Ranga
Keyword(s):  
Machine Learning ◽  
Defense Mechanisms ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Learning Techniques ◽  
New Generation ◽  
Networking Technology

Distributed denial of service (DDoS) attack is one of the most disastrous attacks that compromises the resources and services of the server. DDoS attack makes the services unavailable for its legitimate users by flooding the network with illegitimate traffic. Most commonly, it targets the bandwidth and resources of the server. This chapter discusses various types of DDoS attacks with their behavior. It describes the state-of-the-art of DDoS attacks. An emerging technology named “Software-defined networking” (SDN) has been developed for new generation networks. It has become a trending way of networking. Due to the centralized networking technology, SDN suffers from DDoS attacks. SDN controller manages the functionality of the complete network. Therefore, it is the most vulnerable target of the attackers to be attacked. This work illustrates how DDoS attacks affect the whole working of SDN. The objective of this chapter is also to provide a better understanding of DDoS attacks and how machine learning approaches may be used for detecting DDoS attacks.

DDoS Attacks and Defense Mechanisms Using Machine Learning Techniques for SDN

2020 ◽  
pp. 193-214 ◽  
Author(s):  
Rochak Swami ◽  
Mayank Dave ◽  
Virender Ranga
Keyword(s):  
Machine Learning ◽  
Defense Mechanisms ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Learning Techniques ◽  
New Generation ◽  
Networking Technology

Distributed denial of service (DDoS) attack is one of the most disastrous attacks that compromises the resources and services of the server. DDoS attack makes the services unavailable for its legitimate users by flooding the network with illegitimate traffic. Most commonly, it targets the bandwidth and resources of the server. This chapter discusses various types of DDoS attacks with their behavior. It describes the state-of-the-art of DDoS attacks. An emerging technology named “Software-defined networking” (SDN) has been developed for new generation networks. It has become a trending way of networking. Due to the centralized networking technology, SDN suffers from DDoS attacks. SDN controller manages the functionality of the complete network. Therefore, it is the most vulnerable target of the attackers to be attacked. This work illustrates how DDoS attacks affect the whole working of SDN. The objective of this chapter is also to provide a better understanding of DDoS attacks and how machine learning approaches may be used for detecting DDoS attacks.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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