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Abstract
Technology plays a crucial role in agriculture, and with the Internet of Things (IoT), it has evolved into smart agriculture. The use of this technology for smart farming is increasing worldwide as it provides reliable information about farms, leading to enhanced productivity and the conservation of water and other resources. However, smart farming also faces challenges such as high costs, dependence on technology, and network security, with network and information security in IoT agriculture being among the most critical concerns. One common issue is Distributed Denial of Service (DDoS) attacks, which pose a significant threat to IoT agriculture networks. A DDoS attack in IoT generally refers to the temporary or permanent disruption or suspension of services to a host connected to the IoT agriculture network. This makes the intelligent farming network inaccessible, causing substantial damage to farms and fields. This paper presents a proposed combined method based on deep learning using the RapidMiner program for more accurate classification and detection of attacks in IoT agriculture networks. The proposed method integrates deep learning, Decision Tree, and K-nearest neighbors (KNN) algorithms. The results show that the proposed method performs exceptionally well, achieving an accuracy of 99.82% and an error rate of 0.18%.
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References
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- Fathy, C., & Ali, H. M. (2023). A secure IoT-based irrigation system for precision agriculture using the expeditious cipher. Sensors, 23(4), 2091.
- Ferrag, M. A., Shu, L., Djallel, H., & Choo, K.-K. R. (2021). Deep learning-based intrusion detection for distributed denial of service attack in agriculture 4.0. Electronics, 10(11), 1257.
- GeeksforGeeks. (2022). https://www.geeksforgeeks.org/visualize-confusion-matrix-using-caret-package-in-r/
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- Karagiannis, V., Chatzimisios, P., Vazquez-Gallego, F., & Alonso-Zarate, J. (2015). A survey on application layer protocols for the internet of things. Transaction on IoT and Cloud computing, 3(1), 11-17.
- Kethineni, K., & Gera, P. (2023). Iot-Based Privacy-Preserving Anomaly Detection Model for Smart Agriculture. Systems, 11(6), 304.
- Munir, M. S., Bajwa, I. S., & Cheema, S. M. (2019). An intelligent and secure smart watering system using fuzzy logic and blockchain. Computers & Electrical Engineering, 77, 109-119.
- Quy, V. K., Hau, N. V., Anh, D. V., Quy, N. M., Ban, N. T., Lanza, S., Randazzo, G., & Muzirafuti, A. (2022). IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Applied Sciences, 12(7), 3396.
- Tamanna, T., Fatema, T., & Saha, R. (2017). SDN, A research on SDN assets and tools to defense DDoS attack in cloud computing environment. 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET),
- Vatambeti, R., Venkatesh, D., Mamidisetti, G., Damera, V. K., Manohar, M., & Yadav, N. S. (2023). Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet. Scientific Reports, 13(1), 15371.
- Yang, X., Shu, L., Chen, J., Ferrag, M. A., Wu, J., Nurellari, E., & Huang, K. (2020). A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges. IEEE/CAA Journal of Automatica Sinica, 8, 273-302.
References
Aldhyani, T. H., & Alkahtani, H. (2023). Cyber Security for Detecting Distributed Denial of Service Attacks in Agriculture 4.0: Deep Learning Model. Mathematics, 11(1), 233.
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2006). Greedy layer-wise training of deep networks. Advances in neural information processing systems, 19.
Dahane, A., Benameur, R., & Kechar, B. (2022). An IoT low-cost smart farming for enhancing irrigation efficiency of smallholders farmers. Wireless Personal Communications, 127(4), 3173-3210.
Deng, L. (2014). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA transactions on Signal and Information Processing, 3, e2.
Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 82, 761-768.
Farooq, M. S., Riaz, S., Abid, A., Abid, K., & Naeem, M. A. (2019). A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming. Ieee Access, 7, 156237-156271.
Fathy, C., & Ali, H. M. (2023). A secure IoT-based irrigation system for precision agriculture using the expeditious cipher. Sensors, 23(4), 2091.
Ferrag, M. A., Shu, L., Djallel, H., & Choo, K.-K. R. (2021). Deep learning-based intrusion detection for distributed denial of service attack in agriculture 4.0. Electronics, 10(11), 1257.
GeeksforGeeks. (2022). https://www.geeksforgeeks.org/visualize-confusion-matrix-using-caret-package-in-r/
Jara, A. J., Zamora, M. A., & Skarmeta, A. F. (2009). HWSN6: Hospital wireless sensor networks based on 6LoWPAN technology: Mobility and fault tolerance management. 2009 International conference on computational science and engineering,
Karagiannis, V., Chatzimisios, P., Vazquez-Gallego, F., & Alonso-Zarate, J. (2015). A survey on application layer protocols for the internet of things. Transaction on IoT and Cloud computing, 3(1), 11-17.
Kethineni, K., & Gera, P. (2023). Iot-Based Privacy-Preserving Anomaly Detection Model for Smart Agriculture. Systems, 11(6), 304.
Munir, M. S., Bajwa, I. S., & Cheema, S. M. (2019). An intelligent and secure smart watering system using fuzzy logic and blockchain. Computers & Electrical Engineering, 77, 109-119.
Quy, V. K., Hau, N. V., Anh, D. V., Quy, N. M., Ban, N. T., Lanza, S., Randazzo, G., & Muzirafuti, A. (2022). IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Applied Sciences, 12(7), 3396.
Tamanna, T., Fatema, T., & Saha, R. (2017). SDN, A research on SDN assets and tools to defense DDoS attack in cloud computing environment. 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET),
Vatambeti, R., Venkatesh, D., Mamidisetti, G., Damera, V. K., Manohar, M., & Yadav, N. S. (2023). Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet. Scientific Reports, 13(1), 15371.
Yang, X., Shu, L., Chen, J., Ferrag, M. A., Wu, J., Nurellari, E., & Huang, K. (2020). A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges. IEEE/CAA Journal of Automatica Sinica, 8, 273-302.