TY - GEN
T1 - Autoencoders Based Optimized Deep Learning Model for the Detection of Cyber Attack in IoT Environment
AU - Gupta, Brij B.
AU - Gaurav, Akshat
AU - Chui, Kwok Tai
AU - Arya, Varsha
AU - Choi, Chang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Autoencoders
KW - Cybersecurity
KW - Deep Learning
KW - Internet of Things (IoT)
UR - http://www.scopus.com/inward/record.url?scp=85186985107&partnerID=8YFLogxK
U2 - 10.1109/ICCE59016.2024.10444394
DO - 10.1109/ICCE59016.2024.10444394
M3 - Conference contribution
AN - SCOPUS:85186985107
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2024 IEEE International Conference on Consumer Electronics, ICCE 2024
T2 - 2024 IEEE International Conference on Consumer Electronics, ICCE 2024
Y2 - 6 January 2024 through 8 January 2024
ER -