Improving the IoT Attack Classification Mechanism with Data Augmentation for Generative Adversarial Networks
Source abstract
Abstract
The development of IoT technology has made various IoT applications and services widely used. Because IoT devices have weak information security protection capabilities, they are easy targets for cyber attacks. Therefore, this study proposes MLP-based IoT attack classification with data augmentation for GANs. In situations where the overall classification performance is satisfactory but the performance of a specific class is poor, GANs are employed as a data augmentation mechanism for that class to enhance its classification performance. The experimental results indicate that regardless of whether the training dataset is BoT-IoT or TON-IOT, the proposed method significantly improves the classification performance of classes with insufficient training data when using the data augmentation mechanism with GANs. Furthermore, the classification accuracy, precision, recall, and F1-score performance all exceed 90%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications · Network Security and Intrusion Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
Funding
Endian Hardware Co., Ltd.
References (34)
Show source-provided references
1. Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review10.1109/ACCESS.2021.3073408 ↗
2. (2022, July 20). State of IoT 2022: Number of Connected IoT Devices Growing 18% to 14.4 Billion Globally. Available online: https://iotbusinessnews.com/2022/05/19/70343-state-of-iot-2022-number-of-connected-iot-devices-growing-18-to-14-4-billion-globally.
3. (2023, March 10). 2023 SonicWall Cyber Threat Report. Available online: https://www.sonicwall.com/2023-cyber-threat-report/.
4. Dini, P., Elhanashi, A., Begni, A., Saponara, S., Zheng, Q., and Gasmi, K. (2023). Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity. Appl. Sci., 13.10.3390/app13137507 ↗
5. Host-based IDS: A review and open issues of an anomaly detection system in IoT10.1016/j.future.2022.03.001 ↗
6. Tudor, D. (2021, December 17). What Is a Host Intrusion Detection System (HIDS) and How It Works. Available online: https://heimdalsecurity.com/blog/host-intrusion-detection-system-hids/.
7. Detection of DDoS attacks with feed forward based deep neural network model10.1016/j.eswa.2020.114520 ↗
8. DIDS: A Deep Neural Network based real-time Intrusion detection system for IoT10.1016/j.dajour.2022.100142 ↗
9. Robust detection for network intrusion of industrial IoT based on multi-CNN fusion10.1016/j.measurement.2019.107450 ↗
10. Khan, M.A. (2021). HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System. Processes, 9.10.3390/pr9050834 ↗
11. Design and development of a deep learning-based model for anomaly detection in IoT networks10.1109/ACCESS.2021.3094024 ↗
12. Tareq, I., Elbagoury, B.M., El-Regaily, S., and El-Horbaty, E.S.M. (2022). Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT datasets using dl in cybersecurity for IoT. Appl. Sci., 12.10.3390/app12199572 ↗
13. A deep learning approach for intrusion detection using recurrent neural networks10.1109/ACCESS.2017.2762418 ↗
14. Protocol-Based Deep Intrusion Detection for DoS and DDoS Attacks Using UNSW-NB15 and Bot-IoT Data-Sets10.1109/ACCESS.2021.3137201 ↗
15. Gaur, V., and Kumar, R. (2022, January 10–11). DDoSLSTM: Detection of Distributed Denial of Service Attacks on IoT Devices using LSTM Model. Proceedings of the 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India.10.1109/IC3IOT53935.2022.9767889 ↗
16. DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU)10.1016/j.future.2021.01.022 ↗
17. Yao, W., Hu, L., Hou, Y., and Li, X. (2023). A Lightweight Intelligent Network Intrusion Detection System Using One-Class Autoencoder and Ensemble Learning for IoT. Sensors, 23.10.3390/s23084141 ↗
18. Zhang, H., Zhang, B., Huang, L., Zhang, Z., and Huang, H. (2023). An Efficient Two-Stage Network Intrusion Detection System in the Internet of Things. Information, 14.10.3390/info14020077 ↗
19. Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., and Weinberger, K.Q. (2014). Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, Inc.
20. Zixu, T., Liyanage, K.S.K., and Gurusamy, M. (2020, January 7–11). Generative Adversarial Network and Auto Encoder based Anomaly Detection in Distributed IoT Networks. Proceedings of the GLOBECOM 2020-2020 IEEE Global Communications Conference, Taipei, Taiwan.10.1109/GLOBECOM42002.2020.9348244 ↗
21. A Framework for Anomaly Detection in IoT Networks Using Conditional Generative Adversarial Networks10.1109/ACCESS.2021.3132127 ↗
22. An enhanced ai-based network intrusion detection system using generative adversarial networks10.1109/JIOT.2022.3211346 ↗
23. Chu, H.C., and Lin, Y.J. (2023, January 11–13). IoT Attack Classification Based on Generative Adversarial Networks. Proceedings of the 6th IEEE International Conference on Knowledge Innovation and Invention, Sapporo, Japan.10.3390/app132312592 ↗
24. Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset10.1016/j.future.2019.05.041 ↗
25. Foundation, T.O. (2022, March 15). Node-Red: Low-Code Programming for Event-Driven Applications. Available online: https://nodered.org/.
26. Jekyll (2022, March 16). Ostinato Traffic Generator for Network Engineers. Available online: https://ostinato.org/.
27. González-Granadillo, G., González-Zarzosa, S., and Diaz, R. (2021). Security Information and Event Management (SIEM): Analysis, Trends, and Usage in Critical Infrastructures. Sensors, 21.10.3390/s21144759 ↗
28. A systematic survey of regularization and normalization in GANs
29. Investigating the impact of data normalization on classification performance10.1016/j.asoc.2019.105524 ↗
30. Balancing learning model privacy, fairness, and accuracy with early stopping criteria10.1109/TNNLS.2021.3129592 ↗
31. Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.10.1186/s12864-019-6413-7 ↗
32. Chicco, D., Tötsch, N., and Jurman, G. (2021). The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Mining, 14.10.1186/s13040-021-00244-z ↗
33. A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets10.1016/j.scs.2021.102994 ↗
34. TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems10.1109/ACCESS.2020.3022862 ↗