Efficient Real-Time Anomaly Detection in IoT Networks Using One-Class Autoencoder and Deep Neural Network
Aya G. Ayad, M. M. El-Gayar, Noha A. Hikal, Nehal A. Sakr
Abstract
In the face of growing Internet of Things (IoT) security challenges, traditional Intrusion Detection Systems (IDSs) fall short due to IoT devices’ unique characteristics and constraints. This paper presents an effective, lightweight detection model that strengthens IoT security by addressing the high dimensionality of IoT data. This model merges an asymmetric stacked autoencoder with a Deep Neural Network (DNN), applying one-class learning. It achieves a high detection rate with minimal false positives in a short time. Compared with state-of-the-art approaches based on the BoT-IoT dataset, it shows a higher detection rate of up to 96.27% in 0.27 s. Also, the model achieves an accuracy of 99.99%, precision of 99.21%, and f1 score of 97.69%. These results demonstrate the effectiveness and significance of the proposed model, confirming its potential for reliable deployment in real IoT security problems.