Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Ahmed Fahad Alshudukhi
Universiti Kebangsaan Malaysia
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
A feature selection method based on auto-encoder for internet of things intrusion detection Ahmed Fahad Alshudukhi; Saif Ahmed Jabbar; Basel Alshaikhdeeb
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3265-3275

Abstract

The evolution in gadgets where various devices have become connected to the internet such as sensors, cameras, smartphones, and others, has led to the emergence of internet-of-things (IoT). As any network, security is the main issue facing IoT. Several studies addressed the intrusion detection task in IoT. The majority of these studies utilized different statistical and bio-inspired feature selection techniques. Deep learning is a family of techniques that demonstrated remarkable performance in the field of classification. The emergence of deep learning techniques has led to configure new neural network architectures that is designed for the feature selection task. This study proposes a deep learning architecture known as auto-encoder (AE) for the task of feature selection in IoT intrusion detection. A benchmark dataset for IoT intrusions has been considered in the experiments. The proposed AE has been carried out for the feature selection task along with a simple neural network (NN) architecture for the classification task. Experimental results showed that the proposed AE showed an accuracy of 99.97% with a false alarm rate (FAR) of 1.0. The comparison against the state of the art proves the efficacy of AE.
Co-Authors Basel Alshaikhdeeb Saif Ahmed Jabbar