Volume 4 , Issue 1 , PP: 56-68, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Waleed Abd Elkhalik 1 * , Ibrahim Elhenawy 2
Doi: https://doi.org/10.54216/IJWAC.040106
Because of the lightning-fast expansion of the Internet of Things (IoT) technologies, an enormous amount of data has been produced. This traffic can be mined for information that can be used to identify and avoid intrusions into IoT networks. Despite the significant efforts that have been put into labeling Internet of Things traffic records, the total number of labeled records is still quite low, which makes it more difficult to detect intrusions. This study introduces a semi-supervised deep learning approach for intrusion detection (S2T-Net), in which we propose a temporal transformer module to empower the model to learn valuable interactions in cellular data. An improved spatial transformer is presented to capture local representation in the cellular traffic flow. At the same time, a multilevel semi-supervised training technique is used to account for the consecutive structure of the IoT traffic information. In order to provide effective real-time threat intelligence, the suggested S2T-Net can be tightly coupled into a cellular IoT network. Last but not least, empirical assessments on two current databases (CIC-IDS2017 and CIC-IDS2018) show that S2T-Net boosts intrusion detection accuracy and resilience while retaining resource-efficient computing.
Cellular Networks , Internet of Things (IoT) , Deep Learning , Semi-supervised Learning , Anomaly Detection , Security.
[1] D. Breitenbacher, I. Homoliak, Y. L. Aung, Y. Elovici, and N. O. Tippenhauer, “HADES-IoT: A Practical and Effective Host-Based Anomaly Detection System for IoT Devices (Extended Version),” IEEE Internet Things J., 2022, doi: 10.1109/JIOT.2021.3135789.
[2] M. Savic et al., “Deep Learning Anomaly Detection for Cellular IoT with Applications in Smart Logistics,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3072916.
[3] A. A. Cook, G. Misirli, and Z. Fan, “Anomaly Detection for IoT Time-Series Data: A Survey,” IEEE Internet of Things Journal. 2020, doi: 10.1109/JIOT.2019.2958185.
[4] C. Yin, S. Zhang, J. Wang, and N. N. Xiong, “Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series,” IEEE Trans. Syst. Man, Cybern. Syst., 2022, doi: 10.1109/TSMC.2020.2968516.
[5] L. Cui et al., “Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures,” IEEE Trans. Ind. Informatics, 2022, doi: 10.1109/TII.2021.3107783.
[6] R. Li, Q. Li, J. Zhou, and Y. Jiang, “ADRIoT: An Edge-Assisted Anomaly Detection Framework Against IoT-Based Network Attacks,” IEEE Internet Things J., 2022, doi: 10.1109/JIOT.2021.3122148.
[7] Y. An, F. R. Yu, J. Li, J. Chen, and V. C. M. Leung, “Edge Intelligence (EI)-Enabled HTTP Anomaly Detection Framework for the Internet of Things (IoT),” IEEE Internet Things J., 2021, doi: 10.1109/JIOT.2020.3024645.
[8] T. B. Dang, D. T. Le, T. D. Nguyen, M. Kim, and H. Choo, “Monotone Split and Conquer for Anomaly Detection in IoT Sensory Data,” IEEE Internet Things J., 2021, doi: 10.1109/JIOT.2021.3073705.
[9] Z. Chen, D. Chen, X. Zhang, Z. Yuan, and X. Cheng, “Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT,” IEEE Internet Things J., 2022, doi: 10.1109/JIOT.2021.3100509.
[10] M. O. Osifeko, G. P. Hancke, and A. M. Abu-Mahfouz, “SurveilNet: A Lightweight Anomaly Detection System for Cooperative IoT Surveillance Networks,” IEEE Sens. J., 2021, doi: 10.1109/JSEN.2021.3103016.
[11] R. Zhao et al., “A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things,” IEEE Internet Things J., 2022, doi: 10.1109/JIOT.2021.3119055.
[12] M. Abdel-Basset, V. Chang, H. Hawash, R. K. Chakrabortty, and M. Ryan, “Deep-IFS: Intrusion Detection Approach for Industrial Internet of Things Traffic in Fog Environment,” IEEE Trans. Ind. Informatics, 2021, doi: 10.1109/TII.2020.3025755.
[13] Y. Cheng, Y. Xu, H. Zhong, and Y. Liu, “Leveraging Semisupervised Hierarchical Stacking Temporal Convolutional Network for Anomaly Detection in IoT Communication,” 2021, doi: 10.1109/JIOT.2020.3000771.
[14] Y. Lu et al., “Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks,” IEEE Trans. Veh. Technol., 2020, doi: 10.1109/TVT.2020.2995160.
[15] Q. Xie, P. Zhang, B. Yu, and J. Choi, “Semisupervised Training of Deep Generative Models for High-Dimensional Anomaly Detection,” IEEE Trans. Neural Networks Learn. Syst., 2022, doi: 10.1109/TNNLS.2021.3095150.
[16] K. Kumaran Santhosh, D. P. Dogra, P. P. Roy, and A. Mitra, “Vehicular Trajectory Classification and Traffic Anomaly Detection in Videos Using a Hybrid CNN-VAE Architecture,” IEEE Trans. Intell. Transp. Syst., 2022, doi: 10.1109/TITS.2021.3108504.
[17] M. Abdel-Basset, H. Hawash, R. K. Chakrabortty, and M. J. Ryan, “Semi-Supervised Spatiotemporal Deep Learning for Intrusions Detection in IoT Networks,” IEEE Internet Things J., 2021, doi: 10.1109/JIOT.2021.3060878.
[18] Y. Guo, T. Ji, Q. Wang, L. Yu, G. Min, and P. Li, “Unsupervised Anomaly Detection in IoT Systems for Smart Cities,” IEEE Trans. Netw. Sci. Eng., 2020, doi: 10.1109/TNSE.2020.3027543.
[19] G. Muhammad, M. S. Hossain, and S. Garg, “Stacked Autoencoder-based Intrusion Detection System to Combat Financial Fraudulent,” IEEE Internet Things J., 2020, doi: 10.1109/JIOT.2020.3041184.
[20] H. M. Song and H. K. Kim, “Self-Supervised Anomaly Detection for In-Vehicle Network Using Noised Pseudo Normal Data,” IEEE Trans. Veh. Technol., 2021, doi: 10.1109/TVT.2021.3051026.
[21] A. Vaswani et al., “Attention is all you need,” 2017.
[22] I. Hafeez, M. Antikainen, A. Y. Ding, and S. Tarkoma, “IoT-KEEPER: Detecting Malicious IoT Network Activity Using Online Traffic Analysis at the Edge,” IEEE Trans. Netw. Serv. Manag., 2020, doi: 10.1109/TNSM.2020.2966951.
[23] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” 2018, doi: 10.5220/0006639801080116.
[24] X. Xu, J. Li, Y. Yang, and F. Shen, “Toward Effective Intrusion Detection Using Log-Cosh Conditional Variational Autoencoder,” IEEE Internet Things J., 2021, doi: 10.1109/JIOT.2020.3034621.
[25] P. Virtanen et al., “SciPy 1.0: fundamental algorithms for scientific computing in Python,” Nat. Methods, 2020, doi: 10.1038/s41592-019-0686-2.
