Efficient Approach for Anomaly Detection in Internet of Things Traffic Using Deep Learning
Syed Ibrahim Imtiaz, Liaqat Ali Khan, Ahmad Almadhor, Sidra Abbas, Shtwai Alsubai, Michal Greguš, Zunera Jalil
Abstract
The network intrusion detection system (NIDs) is a significant research milestone in information security. NIDs can scan and analyze the network to detect an attack or anomaly, which may be a continuing intrusion or perhaps an intrusion that has just occurred. During the pandemic, cybercriminals realized that home networks lurked with vulnerabilities due to a lack of security and computational limitations. A fundamental difficulty in NIDs is providing an effective, robust, lightweight, and rapid framework to perform real‐time intrusion detection. This research proposes an efficient, functional cybersecurity approach based on machine/deep learning algorithms to detect anomalies using lightweight network‐based IDs. A lightweight, real‐time, network‐based anomaly detection system can be used to secure connected IoT devices. The UNSW‐NB15 dataset is used to evaluate the proposed approach DeepNet and compare results alongside other state‐of‐the‐art existing techniques. For the classification of network‐based anomalies, the proposed model achieves 99.16% accuracy by using all features and 99.14% accuracy after feature reduction. The experimental results show that the network anomalies depend exceptionally on features selected after selection.