Autoencoders Based Optimized Deep Learning Model for the Detection of Cyber Attack in IoT Environment

The Internet of Things (IoTs) are a smart devices that connect the cyber and physical worlds. Nowadays, IoT has applications in many domains. However, various threats could disrupt IoT activities. In order to identify cyber attacks in an IoT environment, this study introduced a deep learning and aut...

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Vydané v:Proceedings of IEEE International Symposium on Consumer Electronics s. 1 - 6
Hlavní autori: Gupta, Brij B., Gaurav, Akshat, Chui, Kwok Tai, Arya, Varsha, Choi, Chang
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 06.01.2024
Predmet:
Autoencoders
Computational modeling
Cyberattack
Cybersecurity
Deep learning
Feature extraction
Internet of Things
Internet of Things (IoT)
Performance evaluation
Smart devices
ISSN:2158-4001
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Popis
Shrnutí:The Internet of Things (IoTs) are a smart devices that connect the cyber and physical worlds. Nowadays, IoT has applications in many domains. However, various threats could disrupt IoT activities. In order to identify cyber attacks in an IoT environment, this study introduced a deep learning and autoencoder-based model. The performance of the prospered approach is calculated by accuracy, recall, and f1 score. Our prospered approach is 90% accuracy in detecting malicious traffic.
ISSN:2158-4001
DOI:10.1109/ICCE59016.2024.10444394