scholarly journals Implementation of Entropy-based Distributed Denial of Service Attack Detection Method in Multiple POX Controllers

2019 ◽  
Vol 6 (2) ◽  
pp. 29-38
Author(s):  
Mahmood Abdullah ◽  
Nasir Al-awad ◽  
Fatima Hussein
Keyword(s):  
Detection Method ◽  
Denial Of Service ◽  
Attack Detection ◽  
Distributed Denial Of Service ◽  
Denial Of Service Attack ◽  
Service Attack

scholarly journals Noise-Robust Multilayer Perceptron Architecture for Distributed Denial of Service Attack Detection

2020 ◽  
pp. 1-1
Author(s):  
Joao Paulo A. Maranhao ◽  
Joao Paulo C. L. da Costa ◽  
Edison Pignaton de Freitas ◽  
Elnaz Javidi ◽  
Rafael T. de Sousa
Keyword(s):  
Multilayer Perceptron ◽  
Denial Of Service ◽  
Attack Detection ◽  
Distributed Denial Of Service ◽  
Denial Of Service Attack ◽  
Service Attack ◽  
Noise Robust

scholarly journals Detection of Distributed Denial of Service Flooding Attack Using Odds Ratio

2021 ◽  
Vol 1 (2) ◽  
pp. 67-74
Author(s):  
Dalia Nashat ◽  
Fatma A. Hussain ◽  
Xiaohong Jiang
Keyword(s):  
Detection Probability ◽  
Odds Ratio ◽  
Detection Method ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Internet Routing ◽  
Denial Of Service Attack ◽  
Flooding Attack ◽  
Flooding Attacks ◽  
Service Attack

Computer networks are vulnerable to many types of attacks while the Distributed Denial of Service attack (DDoS) serves as one of the top concerns for security professionals. The DDoS flooding attack denies the services by consuming the server resources to prevent the legitimate users from using their desired services. The hardness of detecting this attack lies in sending a stream of packets to the server with spoofed IP addresses, so that the internet routing infrastructure cannot distinguish the spoofed packets. Based on the odds ratio (OR) statistical measurement, in this work we propose a new detection method for the DDoS flooding attacks. By exploring the odds ratio to determine the risk factor of any incoming traffic to the server, the legitimate and attack traffic packets can be easily differentiated. Experimental results demonstrate the efficiency of the presented detection method in terms of its detection probability and detection time.

Tensor based framework for Distributed Denial of Service attack detection

2021 ◽  
Vol 174 ◽  
pp. 102894
Author(s):  
João Paulo A. Maranhão ◽  
João Paulo C.L. da Costa ◽  
Elnaz Javidi ◽  
César A. Borges de Andrade ◽  
Rafael T. de Sousa
Keyword(s):  
Denial Of Service ◽  
Attack Detection ◽  
Distributed Denial Of Service ◽  
Denial Of Service Attack ◽  
Service Attack
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