Semi-Supervised Spatiotemporal Deep Learning for Intrusions Detection in IoT Networks
- 19 February 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Internet of Things Journal
- Vol. 8 (15), 12251-12265
- https://doi.org/10.1109/jiot.2021.3060878
Abstract
The rapid growth of the Internet of Things (IoT) technologies has generated a huge amount of traffic that can be exploited for detecting intrusions through IoT networks. Despite the great effort made in annotating IoT traffic records, the number of labeled records is still very small, increasing the difficulty in recognizing attacks and intrusions. This study introduces a semi-supervised deep learning approach for intrusion detection (SS-Deep-ID), in which we propose a multi-scale residual temporal convolutional (MS-Res) module to finetune the network capability in learning Spatio-temporal representations. An improved traffic attention (TA) mechanism is introduced to estimate the importance score that helps the model to concentrate on important information during learning. Furthermore, a hierarchical semi-supervised training method is introduced which takes into account the sequential characteristics of the IoT traffic data during training. The proposed SS-Deep-ID easily integrated into Fog-enabled IoT network to offer efficient real-time intrusion detection. Lastly, empirical evaluations on two recent datasets (CIC-IDS2017 and CIC-IDS2018) demonstrate that SS-Deep-ID improves the efficiency of intrusion detection and increases the robustness of performance while maintaining computational efficiency.Keywords
This publication has 45 references indexed in Scilit:
- Unsupervised Anomaly Detection With LSTM Neural NetworksIEEE Transactions on Neural Networks and Learning Systems, 2020
- A Supervised Intrusion Detection System for Smart Home IoT DevicesIEEE Internet of Things Journal, 2019
- Res2Net: A New Multi-Scale Backbone ArchitectureIEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
- $L1$ -Norm Batch Normalization for Efficient Training of Deep Neural NetworksIEEE Transactions on Neural Networks and Learning Systems, 2018
- IoT Security Techniques Based on Machine Learning How do IoT devices use AI to enhance security?IEEE Signal Processing Magazine, 2018
- Information Dropout: Learning Optimal Representations Through Noisy ComputationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2018
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Semi-supervised learning combining transductive support vector machine with active learningNeurocomputing, 2016
- Feature Ranking in Intrusion Detection Dataset using Combination of Filtering MethodsInternational Journal of Computer Applications, 2013
- Fuzzy support vector machinesIEEE Transactions on Neural Networks, 2002