Real-time detection of distributed denial-of-service attacks using RBF networks and statistical features

2005 ◽  
Vol 48 (2) ◽  
pp. 235-245 ◽  
Author(s):  
Dimitris Gavrilis ◽  
Evangelos Dermatas
Keyword(s):  
Real Time ◽  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Statistical Features ◽  
Rbf Networks ◽  
Real Time Detection

scholarly journals A Proposal of Highly Responsive Distributed Denial-of-Service Attacks Detection Using Real-Time Burst Detection Method

2018 ◽  
Vol 26 (0) ◽  
pp. 257-266
Author(s):  
Shotaro Usuzaki ◽  
Yuki Arikawa ◽  
Hisaaki Yamaba ◽  
Kentaro Aburada ◽  
Shin-Ichiro Kubota ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Burst Detection

Real-Time Detection of Denial-of-Service Attacks in IEEE 802.11p Vehicular Networks

2014 ◽  
Vol 18 (1) ◽  
pp. 110-113 ◽  
Author(s):  
Nikita Lyamin ◽  
Alexey Vinel ◽  
Magnus Jonsson ◽  
Jonathan Loo
Keyword(s):  
Real Time ◽  
Vehicular Networks ◽  
Denial Of Service ◽  
Ieee 802.11P ◽  
Denial Of Service Attacks ◽  
Real Time Detection

Conceptual Framework to Mitigate Internet of Things-DDoS attacks using Fog Nodes

2020 ◽  
Vol 14 ◽  
Author(s):  
B.B. Gupta ◽  
Harish S.A.
Keyword(s):  
Internet Of Things ◽  
Conceptual Framework ◽  
Real Time ◽  
Autonomous System ◽  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Technology Infrastructure ◽  
Information Technology Infrastructure ◽  
Remote Server

: Internet of Things has proven to maximize workflow and data sensing capabilities. Contrarily, Distributed Denial of Service attacks that employ compromised Internet of Things devices have caused considerable damage to the Information Technology infrastructure since their advent. More specifically, Application level attacks facilitated using affected Internet of Things devices are found to be difficult to detect and defend against. Seemingly benign traffic from infected devices exit the network edge to target a remote server. The conceptual framework described in this paper attempts to mitigate malicious Internet of Things traffic at the source network. Fog nodes at the source Autonomous System are utilized in tandem with a ratio metric that flags traffic as well as the originating device as suspicious or benign based on traffic ratios calculated in real time. Subsequently, malicious traffic is blocked inside the Autonomous System. The burden and risk of protecting the external network from Distributed Denial of Service attacks is transferred to Fog nodes inherent to every source network. The proposed conceptual defense framework is proactive and performs in real time attack scenarios.

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