Naslov
Deep Learning Based Approach for Secure Web of Things (WoT)

Autori
Gaurav, Akshat ; Gupta, Brij ; Hsu, Ching-Hsien ; Peraković, Dragan ; Penalvo, Francisco Jose Garcia

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2021 IEEE International Conference on Communications Workshops (ICC Workshops) / - Denvers (MA) : Institute of Electrical and Electronics Engineers (IEEE), 2021, 1-6

ISBN
978-1-7281-9441-7

Skup
IEEE International Conference on Communications (IEEE ICC2021)

Mjesto i datum
Montréal, Kanada, 14.07.2021. - 23.07.2021

Vrsta sudjelovanja
Radionica

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
WoT ; IoT ; DDoS ; Machine Learning ; Deep Learning

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Tehnologija prometa i transport, Temeljne tehničke znanosti, Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti

POVEZANOST RADA