Abstract
The research aims to create an intelligent Intrusion Detection System (IDS) for Cyber-Physical Production Systems (CPPS) that uses machine learning approaches to identify Distributed Denial-of-Service (DDoS) attacks. The proposed approach trains and compares the performance of Random Forest (RF) and Deep Neural Networks (DNN). To train the various models, the dataset is first pre-processed by feature selection, normalisation, and splitting. Fast classification and interpretability are enabled by the RF model, while deep feature learning is used by the Deep Neural Networks model to identify intricate attack patterns. The Random Forest and Deep Neural Networks models achieved high accuracy scores of 98.2 and 99.3%, respectively, and low false positive rates, according to experimental assessments on benchmark datasets. These results show that the Deep Neural Networks based Intrusion Detection System is a good option for real-time industrial security applications as it effectively protects CPPS from changing cyberthreats.
References
Hagar, Abdulnaser A., and Bharti W. Gawali. "Deep learning for improving attack detection system using CSE-CICIDS2018." NeuroQuantology 20, no. 6 (2022).
Yaras, S.; Dener, M. IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm. Electronics 2024, 13, 1053.
Sudar, K.M.; Beulah, M.; Deepalakshmi, P.; Nagaraj, P.; Chinnasamy, P. Detection of Distributed Denial of Service Attacks in SDN using Machine learning techniques. In Proceedings of the 2021 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 27–29 January 2021; pp. 1–5.
Santos, Reneilson, Danilo Souza, Walter Santo, Admilson Ribeiro, and Edward Moreno. "Machine learning algorithms to detect DDoS attacks in SDN." Concurrency and Computation: Practice and Experience 32, no. 16 (2020): e5402.
Celesova, B.; Val’ko, J.; Grezo, R.; Helebrandt, P. Enhancing security of SDN focusing on control plane and data plane. In Proceedings of the 2019 7th International Symposium on Digital Forensics and Security (ISDFS), Barcelos, Portugal, 10–12 June 2019; pp. 1–6.
Gadze, James Dzisi, Akua Acheampomaa Bamfo-Asante, Justice Owusu Agyemang, Henry Nunoo-Mensah, and Kwasi Adu-Boahen Opare. "An investigation into the application of deep learning in the detection and mitigation of DDOS attack on SDN controllers." Technologies 9, no. 1 (2021): 14.
Singh, Amardeep, and Julian Jang-Jaccard. "Autoencoder-based unsupervised intrusion detection using multi-scale convolutional recurrent networks." arXiv preprint arXiv:2204.03779 (2022).
The UNSW-NB15 Dataset|UNSW Research. Available online: https://research.unsw.edu.au/projects/unsw-nb15-dataset (accessed on 6 February 2025).
NSL-KDD|Datasets|Research|Canadian Institute for Cybersecurity|UNB. Available online: https://www.unb.ca/cic/datasets/nsl.html (accessed on 6 February 2025).
Monostori, László. "Cyber-physical production systems: roots from manufacturing science and technology." at-Automatisierungstechnik 63, no. 10 (2015): 766-776.
Zheng, Yu, Zheng Li, Xiaolong Xu, and Qingzhan Zhao. "Dynamic defenses in cyber security: Techniques, methods and challenges." Digital Communications and Networks 8, no. 4 (2022): 422-435.
Liu, Hongyu, and Bo Lang. "Machine learning and deep learning methods for intrusion detection systems: A survey." applied sciences 9, no. 20 (2019): 4396.
Ridzuan, Fakhitah, and Wan Mohd Nazmee Wan Zainon. "A review on data cleansing methods for big data." Procedia Computer Science 161 (2019): 731-738.
Oyetoro, Amos, Joseph Mart, and Ugochukwu Amah. "Using Machine Learning Techniques Random Forest and Neural Network to Detect Cyber Attacks." ScienceOpen Preprints (2023).
Han, Hyojoon, Hyukho Kim, and Yangwoo Kim. "Correlation between deep neural network hidden layer and intrusion detection performance in IoT intrusion detection system." Symmetry 14, no. 10 (2022): 2077.
Kim, Jiyeon, Jiwon Kim, Hyunjung Kim, Minsun Shim, and Eunjung Choi. "CNN-based network intrusion detection against denial-of-service attacks." Electronics 9, no. 6 (2020): 916.
Najar, Ashfaq Ahmad, and S. Manohar Naik. "DDoS attack detection using MLP and Random Forest Algorithms." International Journal of Information Technology 14, no. 5 (2022): 2317-2327.
Ortet Lopes, Ivandro, Deqing Zou, Francis A. Ruambo, Saeed Akbar, and Bin Yuan. "Towards effective detection of recent DDoS attacks: A deep learning approach." Security and Communication Networks 2021, no. 1 (2021): 5710028.