An end-to-end intrusion detection system with IoT dataset using deep learning with unsupervised feature extraction

The rapid growth of the Internet of things (IoT) platform has implications on security vulnerabilities that need to be resolved. This requires an intrusion detection system (IDS) to secure attacks on the platforms. In line with this, numerous machine and deep learning algorithms have been adopted to detect cyber-attacks. Real-time IoT devices transmit massive amounts of heterogeneous data, which affects the network. Traffic networks generate redundant and large amounts of data that must be reduced before processing. This study proposed a hybrid deep learning model for an IDS on the IoT platform. We used unsupervised approaches to extract data dimensions and features, then a neural network for classification. Several approaches were used to determine the effectiveness of the deep learning-based IoT IDS with two scenarios of feature extraction. The first case used autoencoder variants such as deep autoencoder (DAE), deep LSTM autoencoder (LSTM-DAE), and deep convolutional autoencoder. The second case used stacked models for feature extraction, including stacked autoencoder and deep belief network. The feature extraction output from the five models was fine-tuned to the fully connected layer using the BoT-IoT dataset. The results showed a good detection performance of almost 100% and a false positive rate (FPR) of nearly 0%. On the CSE-CIC-IDS2018 dataset, the proposed deep learning model was evaluated using a transfer learning approach with the highest detection rate of 99.17% and the lowest FPR of 0.18%. The model developed from the feature extraction process recognized attacks significantly better than the previous approach.