Detection of Low-rate DDoS Attack Based on Self-Similarity

2010 ◽  
Author(s):  
Zhang Sheng ◽  
Zhang Qifei ◽  
Pan Xuezeng ◽  
Zhu Xuhui
Keyword(s):  
Self Similarity ◽  
Ddos Attack ◽  
Low Rate

scholarly journals Detection of the botnets’ low-rate DDoS attacks based on self-similarity

2020 ◽  
Vol 10 (4) ◽  
pp. 3651
Author(s):  
Sergii Lysenko ◽  
Kira Bobrovnikova ◽  
Serhii Matiukh ◽  
Ivan Hurman ◽  
Oleg Savenko
Keyword(s):  
Network Traffic ◽  
Detection System ◽  
Similarity Analysis ◽  
Ddos Attacks ◽  
Self Similarity ◽  
Detection Process ◽  
Ddos Attack ◽  
Knowledge Formation ◽  
Hurst Coefficient ◽  
Low Rate

An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system – BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process consists of the knowledge formation based on the features that may indicate low-rate DDoS attack performed by a botnet; network monitoring, which analyzes information obtained from the network and making conclusion about possible DDoS attack in the network; and the appliance of the security scenario for the corporate area network’s infrastructure in the situation of low-rate attacks.

Detecting DDOS attack based on network self-similarity

2004 ◽  
Vol 151 (3) ◽  
pp. 292 ◽  
Author(s):  
Y. Xiang ◽  
Y. Lin ◽  
W.L. Lei ◽  
S.J. Huang
Keyword(s):  
Self Similarity ◽  
Ddos Attack
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