Research Article
Year 2023,
Volume: 11 Issue: 2, 321 - 328, 23.06.2023
Abstract
References
- [1] Nandy S., Adhikari M., Khan M. A., Menon V. G., Verma S., An intrusion detection mechanism for secured IoMT framework based on swarm-neural network, IEEE Journal of Biomedical and Health Informatics, 26 (2021) 1969-1976.
- [2] Ahmad J., Shah S. A., Latif S., Ahmed F., Zou Z., Pitropakis N., DRaNN_PSO: A deep random neural network with particle swarm optimization for intrusion detection in the industrial internet of things, Journal of King Saud University-Computer and Information Sciences,(2022).
- [3] Lu K. D., Zeng G. Q., Luo X., Weng J., Luo W., Wu Y., Evolutionary deep belief network for cyber-attack detection in industrial automation and control system, IEEE Transactions on Industrial Informatics, 17 (2021) 7618-7627.
- [4] Campos E. M., Saura P. F., González-Vidal A., Hernández-Ramos J. L., Bernabe J. B., Baldini G., Skarmeta A., Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges, Computer Networks,(2021).
- [5] Alsaedi A., Moustafa N., Tari Z., Mahmood A., Anwar A., TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems, IEEE Access, 8 (2020) 165130-165150.
- [6] Essop I., Ribeiro J. C., Papaioannou M., Zachos G., Mantas G., Rodriguez J., Generating datasets for anomaly-based intrusion detection systems in iot and industrial iot networks, Sensors, 21 (2021) 1528.
- [7] Zachos G., Essop I., Mantas G., Porfyrakis K., Ribeiro J. C., Rodriguez J., An anomaly-based intrusion detection system for internet of medical things networks, Electronics, 10 (2021) 2562.
- [8] Weinger B., Kim J., Sim A., Nakashima M., Moustafa N., Wu K. J., Enhancing IoT anomaly detection performance for federated learning, Digital Communications and Networks,(2022).
- [9] Bui H. K., Lin Y. D., Hwang R. H., Lin P. C., Nguyen V. L., Lai Y. C., CREME: A toolchain of automatic dataset collection for machine learning in intrusion detection, Journal of Network and Computer Applications, 193 (2021) 103212.
- [10] Haider W., Moustafa N., Keshk M., Fernandez A., Choo K. K. R., Wahab A., FGMC-HADS: Fuzzy Gaussian mixture-based correntropy models for detecting zero-day attacks from linux systems, Computers & Security, 96 (2020) 101906.
- [11] Gad A. R., Nashat A. A., Barkat T. M., Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset, IEEE Access, 9 (2021) 142206-142217.
- [12] Idrissi I., Azizi M., Moussaoui, O., Accelerating the update of a DL-based IDS for IoT using deep transfer learning, Indones. J. Electr. Eng. Comput. Sci., 23 (2021) 1059-1067.
- [13] Zhang Z., Zhang Y., Guo D., Song, M., A scalable network intrusion detection system towards detecting, discovering, and learning unknown attacks, International Journal of Machine Learning and Cybernetics, 12 (2021) 1649-1665.
- [14] Al Daoud E., Comparison between XGBoost, LightGBM and CatBoost using a home credit dataset, International Journal of Computer and Information Engineering, 13 (2019) 6-10.
- [15] Mohindru G., Mondal K., Banka, H., Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data, CAAI Transactions on Intelligence Technology, 6 (2021) 405-416.
Year 2023,
Volume: 11 Issue: 2, 321 - 328, 23.06.2023
Abstract
The Internet of Things (IoT) is one of the technologies used in many fields today. Cyber attacks against IoT/Industrial IoT (IIoT) networks, which are increasingly used thanks to the convenience it provides, are constantly increasing. Detection of attacks against IoT/IIoT networks is one of the popular topics recently. The development of a dataset for IoT applications is essential for the intrusion detection in IoT networks. In this context, the ToN_IoT dataset created in the laboratory of UNSW Canberra (Australia) is one of the most comprehensive datasets that can be used to detect cyber attacks on IoT networks. In this study, fridge, garage door, GPS tracker, modbus, motion light, weather, thermostat datasets related to IoT sensors from ToN_IoT datasets were used. The datasets used were subjected to multi-class classification with the Light Gradient Boosting Machine (LGBM) classifier proposed in the study. The obtained results were compared with the literature and it was seen that the proposed method provided the highest classification performance in the literature. It has been determined that the proposed method is effective in preventing cyber attacks on IoT/IIoT networks.
Keywords
References
- [1] Nandy S., Adhikari M., Khan M. A., Menon V. G., Verma S., An intrusion detection mechanism for secured IoMT framework based on swarm-neural network, IEEE Journal of Biomedical and Health Informatics, 26 (2021) 1969-1976.
- [2] Ahmad J., Shah S. A., Latif S., Ahmed F., Zou Z., Pitropakis N., DRaNN_PSO: A deep random neural network with particle swarm optimization for intrusion detection in the industrial internet of things, Journal of King Saud University-Computer and Information Sciences,(2022).
- [3] Lu K. D., Zeng G. Q., Luo X., Weng J., Luo W., Wu Y., Evolutionary deep belief network for cyber-attack detection in industrial automation and control system, IEEE Transactions on Industrial Informatics, 17 (2021) 7618-7627.
- [4] Campos E. M., Saura P. F., González-Vidal A., Hernández-Ramos J. L., Bernabe J. B., Baldini G., Skarmeta A., Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges, Computer Networks,(2021).
- [5] Alsaedi A., Moustafa N., Tari Z., Mahmood A., Anwar A., TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems, IEEE Access, 8 (2020) 165130-165150.
- [6] Essop I., Ribeiro J. C., Papaioannou M., Zachos G., Mantas G., Rodriguez J., Generating datasets for anomaly-based intrusion detection systems in iot and industrial iot networks, Sensors, 21 (2021) 1528.
- [7] Zachos G., Essop I., Mantas G., Porfyrakis K., Ribeiro J. C., Rodriguez J., An anomaly-based intrusion detection system for internet of medical things networks, Electronics, 10 (2021) 2562.
- [8] Weinger B., Kim J., Sim A., Nakashima M., Moustafa N., Wu K. J., Enhancing IoT anomaly detection performance for federated learning, Digital Communications and Networks,(2022).
- [9] Bui H. K., Lin Y. D., Hwang R. H., Lin P. C., Nguyen V. L., Lai Y. C., CREME: A toolchain of automatic dataset collection for machine learning in intrusion detection, Journal of Network and Computer Applications, 193 (2021) 103212.
- [10] Haider W., Moustafa N., Keshk M., Fernandez A., Choo K. K. R., Wahab A., FGMC-HADS: Fuzzy Gaussian mixture-based correntropy models for detecting zero-day attacks from linux systems, Computers & Security, 96 (2020) 101906.
- [11] Gad A. R., Nashat A. A., Barkat T. M., Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset, IEEE Access, 9 (2021) 142206-142217.
- [12] Idrissi I., Azizi M., Moussaoui, O., Accelerating the update of a DL-based IDS for IoT using deep transfer learning, Indones. J. Electr. Eng. Comput. Sci., 23 (2021) 1059-1067.
- [13] Zhang Z., Zhang Y., Guo D., Song, M., A scalable network intrusion detection system towards detecting, discovering, and learning unknown attacks, International Journal of Machine Learning and Cybernetics, 12 (2021) 1649-1665.
- [14] Al Daoud E., Comparison between XGBoost, LightGBM and CatBoost using a home credit dataset, International Journal of Computer and Information Engineering, 13 (2019) 6-10.
- [15] Mohindru G., Mondal K., Banka, H., Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data, CAAI Transactions on Intelligence Technology, 6 (2021) 405-416.
There are 15 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Tasarım ve Teknoloji |
| Authors | |
| Early Pub Date | May 20, 2023 |
| Publication Date | June 23, 2023 |
| Submission Date | September 9, 2022 |
| Published in Issue | Year 2023 Volume: 11 Issue: 2 |
