Anomaly Detection Using Deep Neural Network for IoT Architecture

• Main Authors: Zeeshan, Ahmad, Adnan, Shahid Khan, Kashif, Nisar, Iram, Haider, Rosilah, Hassan, Muhammad Reazul, Haque, Seleviawati, Tarmizi, Joel J. P. C., Rodrigues
• Format: Article
• Language: English
• Published: MDPI 2021
• Subjects: Q Science (General); T Technology (General)
• Online Access: http://ir.unimas.my/id/eprint/37242/1/anomaly1.pdf; http://ir.unimas.my/id/eprint/37242/; https://www.mdpi.com/2076-3417/11/15/7050; https://doi.org/10.3390/app11157050

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed tha numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.