Abstract
The tremendous number of Internet of Things (IoT) applications, with their ubiquity, has provided us with unprecedented productivity and simplified our daily life. At the same time, the insecurity of these technologies ensures that our daily lives are surrounded by vulnerable computers, allowing for the launch of multiple attacks via large-scale botnets through the IoT. These attacks have been successful in achieving their heinous objectives. A strong identification strategy is essential to keep devices secured. This paper proposes and implements a model for anomaly-based intrusion detection in IoT networks that uses a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect and classify binary and multiclass IoT network data. The proposed model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Our proposed binary and multiclass classification model achieved an exceptionally high level of accuracy, precision, recall, and F1 score.
Highlights
We propose a deep learning model based on convolutional neural network (CNN) and gated recurrent unit (GRU)
We have developed a neural network model that utilizes convolutional and gated recurrent units to identify anomalies in Internet of Things (IoT) networks
The IoT-DS-2 dataset is used since it includes all malicious network traffic from the BoT-IoT, IoT network intrusion, MQTT-IoT-IDS2020, and IoT-23 datasets
Summary
Academic Editors: Carlo Giannelli, Marco Picone and Hyun-Ho Choi. The evolution of the IoT network infrastructure has influenced the increasing number of embedded devices and intelligent applications. The IoT objective is to build intelligent environments capable of improving human life quality, comfort, and competitiveness. Devices in smart architectures communicate with one another to execute different tasks. IoT-enabled systems have been used in manufacturing settings as well as for a variety of commercial uses. These intelligent systems include a broad spectrum of capabilities, from smart houses to smart cities, intelligent buildings, and other intelligent utilities such as factory automation and management, power generation networks, and transportation [1]
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