An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks

2021 ◽  
Vol 166 ◽  
pp. 110-124
Author(s):  
Prabhat Kumar ◽  
Govind P. Gupta ◽  
Rakesh Tripathi
2021 ◽  
Vol 11 (16) ◽  
pp. 7228
Author(s):  
Edward Staddon ◽  
Valeria Loscri ◽  
Nathalie Mitton

With the ever advancing expansion of the Internet of Things (IoT) into our everyday lives, the number of attack possibilities increases. Furthermore, with the incorporation of the IoT into Critical Infrastructure (CI) hardware and applications, the protection of not only the systems but the citizens themselves has become paramount. To do so, specialists must be able to gain a foothold in the ongoing cyber attack war-zone. By organising the various attacks against their systems, these specialists can not only gain a quick overview of what they might expect but also gain knowledge into the specifications of the attacks based on the categorisation method used. This paper presents a glimpse into the area of IoT Critical Infrastructure security as well as an overview and analysis of attack categorisation methodologies in the context of wireless IoT-based Critical Infrastructure applications. We believe this can be a guide to aid further researchers in their choice of adapted categorisation approaches. Indeed, adapting appropriated categorisation leads to a quicker attack detection, identification, and recovery. It is, thus, paramount to have a clear vision of the threat landscapes of a specific system.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Moslem Dehghani ◽  
Mohammad Ghiasi ◽  
Taher Niknam ◽  
Abdollah Kavousi-Fard ◽  
Elham Tajik ◽  
...  

Author(s):  
Shanshan Yu ◽  
Jicheng Zhang ◽  
Ju Liu ◽  
Xiaoqing Zhang ◽  
Yafeng Li ◽  
...  

AbstractIn order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.


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