期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2022
卷号：12
期号：3
页码：3265-3275
DOI：10.11591/ijece.v12i3.pp3265-3275
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：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.
关键词：auto-encoder;feature selection;internet of things;intrusion detection;neural network
