Volume 15 , Issue 2 , PP: 225-232, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Amenah A. Jasim 1 , Khattab M. Ali Alheeti 2 *
Doi: https://doi.org/10.54216/JCIM.150217
Industrial Automation and Control Systems (IACS) are necessary for enabling secure information exchange between smart devices; ensuring security in Industrial Control Systems (ICS) is of importance due to the presence of these devices at distant locations and their control over vital plant activities. Intelligent devices and hosts use protocols such as Modbus, DNP3, IEC 60870, IEC 61850, and others. This paper focuses on the analysis and development of techniques for detecting of network traffic within the industrial environment, more specifically anomalies in the application ZZZAlayer in the to the protocol called Distribution Network Protocol (DNP3) is an open-source protocol used in Supervisory Control and Data Acquisition (SCADA) systems and widely recognized as the standard for the water, sewage, and oil and gas industries. it is used in the realm of industrial automation; they are critical facilities for the population and must be secured against any security breaches. One of the main objectives of cyber attackers is related with these systems. In This paper presents an architecture that, classification system by Deep Learning algorithm with (CNN). The proposed model was evaluated using standard Intrusion Detection Dataset for DNP3, with 7326) and 86field. The CNN algorithm obtained the best results accuracy
Cyberattack, DNP3 , ICS , Intrusion Detection , SCADA , Convolutional Neural Network (CNN) , Deep Learning (DL)
[1] V. Kelli et al., “Attacking and defending DNP3 ICS/SCADA systems,” in 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2022, pp. 183–190.
[2] S. Alem, D. Espes, L. Nana, E. Martin, and F. De Lamotte, “A novel bi-anomaly-based intrusion detection system approach for industry 4.0,” Futur. Gener. Comput. Syst., vol. 145, pp. 267–283, 2023.
[3] F. S. Mubarek, S. A. Aliesawi, K. M. A. Alheeti, and N. M. Alfahad, “Urban-AODV: an improved AODV protocol for vehicular ad-hoc networks in urban environment,” Int. J. Eng. Technol., vol. 7, no. 4, pp. 3030–3036, 2018.
[4] A. K. Kareem, A. M. Shaban, A. A. Nafea, M. Aljanabi, S. A. S. Aliesawi, and M. Mal-Ani, “Detecting Routing Protocol Low Power and Lossy Network Attacks Using Machine Learning Techniques,” in 2024 21st International Multi-Conference on Systems, Signals & Devices (SSD), 2024, pp. 57–62.
[5] I. Chakraborty, B. M. Kelley, and B. Gallagher, “Industrial control system device classification using network traffic features and neural network embeddings,” Array, vol. 12, no. July, p. 100081, 2021.
[6] P. M. B, R. Amin, and G. P. Biswas, “A Deep Learning Based Artificial Neural Network Approach for Intrusion Detection,” vol. 1, no. July 2019, pp. 34–43, 2017.
[7] N. S. Mohammed, O. A. Dawood, A. M. Sagheer, and A. A. Nafea, “Secure Smart Contract Based on Blockchain to Prevent the Non-Repudiation Phenomenon,” Baghdad Sci. J., vol. 21, no. 1, p. 234, 2024.
[8] L. Mohammadpour, T. C. Ling, C. S. Liew, and A. Aryanfar, “A Survey of CNN-Based Network Intrusion Detection,” Appl. Sci., vol. 12, no. 16, 2022.
[9] H. M. Song, J. Woo, and H. K. Kim, “In-vehicle network intrusion detection using deep convolutional neural network,” Veh. Commun., vol. 21, p. 100198, 2020.
[10] Z. Li, Z. Qin, K. Huang, X. Yang, and S. Ye, “Intrusion detection using convolutional neural networks for representation learning,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10638 LNCS, pp. 858–866, 2017.
[11] M.-W. Mak and J.-T. Chien, “Omni SCADA Intrusion Detection Using Deep Learning Algorithms,” Mach. Learn. Speak. Recognit., pp. 13–35, 2020.
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DOI: https://doi.org/10.54216/JCIM.150217
Received: May 16, 2024 Revised: July 15, 2024 Accepted: November 04, 2024
[12] P. Sun et al., “DL-IDS: Extracting features using CNN-LSTM hybrid network for intrusion detection system,” Secur. Commun. Networks, vol. 2020, 2020.
[13] S. Y. Diaba and M. Elmusrati, “Proposed algorithm for smart grid DDoS detection based on deep learning,” Neural Networks, vol. 159, pp. 175–184, 2023.
[14] G. Yadav and K. Paul, “Architecture and security of SCADA systems: A review,” Int. J. Crit. Infrastruct. Prot., vol. 34, p. 100433, 2021.
[15] M. A. Ferrag and L. Maglaras, “Deliverycoin: An IDS and blockchain-based delivery framework for drone-delivered services,” Computers, vol. 8, no. 3, pp. 1–15, 2019.
[16] M. Erza and K. Kim, “Deep Learning in Intrusion Detection System : An Overview,” pp. 1–12.
[17] L. Rosa et al., “Intrusion and anomaly detection for the next-generation of industrial automation and control systems,” Futur. Gener. Comput. Syst., vol. 119, pp. 50–67, 2021.
[18] B. Riyaz and S. Ganapathy, “A deep learning approach for effective intrusion detection in wireless networks using CNN,” Soft Comput., vol. 24, no. 22, pp. 17265–17278, 2020.
[19] M. T. Nguyen and K. Kim, “Genetic convolutional neural network for intrusion detection systems,” Futur. Gener. Comput. Syst., vol. 113, pp. 418–427, 2020.
[20] N. N. Jamil and A. K. Kareem, “Comparative Analysis on Machine Learning and One-Dimensional Convolutional Neural Network to Predict Surface Enhanced Raman Spectroscopy,” in 2023 3rd International Conference on Computing and Information Technology (ICCIT), 2023, pp. 216–221.
[21] B. Al-Rami, K. M. A. Alheeti, W. M. Aldosari, S. M. Alshahrani, and S. M. Al-Abrez, “A New Classification Method for Drone-Based Crops in Smart Farming”, Int. J. Interact. Mob. Technol., vol. 16, no. 09, pp. pp. 164–174, May 2022.
[22] F. Z. Belgrana, N. Benamrane, M. A. Hamaida, A. M. Chaabani, and A. Taleb-Ahmed, “Network Intrusion Detection System Using Neural Network and Condensed Nearest Neighbors with Selection of NSL-KDD Influencing Features,” IoTaIS 2020 - Proc. 2020 IEEE Int. Conf. Internet Things Intell. Syst., pp. 23–29, 2021.
[23] D. Prusti and S. K. Rath, “Fraudulent Transaction Detection in Credit Card by Applying Ensemble Machine Learning techniques,” 2019 10th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2019, pp. 1–6, 2019.
[24] Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Trans. Emerg. Telecommun. Technol., vol. 32, no. 1, pp. 1–29, 2021.
[25] S. E. Quincozes, C. Albuquerque, D. Passos, and D. Mossé, “A survey on intrusion detection and prevention systems in digital substations,” Comput. Networks, vol. 184, no. November 2020, 2021.
[26] S. Al-Emadi, A. Al-Mohannadi, and F. Al-Senaid, “Using Deep Learning Techniques for Network Intrusion Detection,” 2020 IEEE Int. Conf. Informatics, IoT, Enabling Technol. ICIoT 2020, pp. 171–176, 2020.
[27] N. Thapa, Z. Liu, D. B. Kc, B. Gokaraju, and K. Roy, “Comparison of machine learning and deep learning models for network intrusion detection systems,” Futur. Internet, vol. 12, no. 10, pp. 1–16, 2020.
[28] S. A. Rafa, Z. M. Al-qfail, A. A. Nafea, S. F. Abd-hood, M. M. Al-Ani, and S. A. Alameri, “A Birds Species Detection Utilizing an Effective Hybrid Model,” in 2024 21st International Multi-Conference on Systems, Signals & Devices (SSD), 2024, pp. 705–710.
[29] K. M. A. Alheeti, A. Alzahrani, M. Alamri, A. K. Kareem, and D. Al_Dosary, “A Comparative Study for SDN Security Based on Machine Learning.” Int. J. Interact. Mob. Technol., vol. 17, no. 11, 2023.
[30] Z. H. Abdaljabar, O. N. Ucan, and K. M. A. Alheeti, “An intrusion detection system for IoT using KNN and decision-tree based classification,” in 2021 International conference of modern trends in information and communication technology industry (MTICTI), 2021, pp. 1–5.
[31] H. J. Mohammed, A. A. Nafea, H. K. Almulla, S. A. S. Aliesawi, and M. M. Al-Ani, “An Effective Hybrid Model for Skin Cancer Detection Using Transfer Learning,” in 2023 16th International Conference on Developments in eSystems Engineering (DeSE), 2023, pp. 840–845.
