Deep Learning Based Approach for Secure Web of Things (WoT) (CROSBI ID 704333)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Gaurav, Akshat ; Gupta, Brij ; Hsu, Ching-Hsien ; Peraković, Dragan ; Penalvo, Francisco Jose Garcia
engleski
Deep Learning Based Approach for Secure Web of Things (WoT)
Internet of Things (IoT) includes smart devices that are connected through a common network, in order to increase the potential of these smart devices, the concept of Web of things (WoT) has introduced. The main aim of WoT is to connect all the smart devices through the internet so that they can share the services and resources globally. But this increase in connectivity makes the devices vulnerable to different types of cyber-attacks. Different types of cyber-attacks like DDoS attacks, DoS attacks, etc., affect the normal operation of smart devices and leak private information, so detection and prevention of cyber-attacks in the WoT is an important research issue. In this paper, we proposed a Deep learning-based approach for the detection of cyber-attacks in the WoTs. We used the KDDCUP99 dataset for training and testing purposes and achieved an accuracy of 99.73%. We also compared our proposed approach with other machine learning approaches and check its effectiveness.
WoT ; IoT ; DDoS ; Machine Learning ; Deep Learning
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Podaci o prilogu
1-6.
2021.
objavljeno
10.1109/ICCWorkshops50388.2021.9473677
Podaci o matičnoj publikaciji
2021 IEEE International Conference on Communications Workshops (ICC Workshops)
Denvers (MA): Institute of Electrical and Electronics Engineers (IEEE)
978-1-7281-9441-7
1983-1883
Podaci o skupu
IEEE International Conference on Communications (IEEE ICC2021)
radionica
14.07.2021-23.07.2021
Montréal, Kanada
Povezanost rada
Informacijske i komunikacijske znanosti, Interdisciplinarne tehničke znanosti, Tehnologija prometa i transport, Temeljne tehničke znanosti