Towards a Hybrid Deep Learning Model for Anomalous Activities Detection in Internet of Things Networks

IoT

Mazhar Sajjad¹  Ayaz Ullah²  Imtiaz Ullah³ 

[1] Department of Computer Science, Comsats University, Islamabad 45550, Pakistan;Department of Computer Science, University of Swabi, Swabi 23430, Pakistan;Department of Electrical, Computer, and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada;

关键词: anomaly detection;    deep learning;    convolutional neural network;    recurrent neural network;    gated recurrent unit;    Internet of Things;   

DOI  :  10.3390/iot2030022

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown