Improvement of intrusion detection system on Industrial Internet of Things based on deep learning using metaheuristic algorithms
Subject Areas : ICTmohammadreza zeraatkarmoghaddam 1 , majid ghayori 2
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Keywords: intrusion detection system, Industrial Internet of Things, metaheuristic algorithms, neural networks,
Abstract :
Due to the increasing use of industrial Internet of Things (IIoT) systems, one of the most widely used security mechanisms is intrusion detection system (IDS) in the IIoT. In these systems, deep learning techniques are increasingly used to detect attacks, anomalies or intrusions. In deep learning, the most important challenge for training neural networks is determining the hyperparameters in these networks. To overcome this challenge, we have presented a hybrid approach to automate hyperparameter tuning in deep learning architecture by eliminating the human factor. In this article, an IDS in IIoT based on convolutional neural networks (CNN) and recurrent neural network based on short-term memory (LSTM) using metaheuristic algorithms of particle swarm optimization (PSO) and Whale (WOA) is used. This system uses a hybrid method based on neural networks and metaheuristic algorithms to improve neural network performance and increase detection rate and reduce neural network training time. In our method, considering the PSO-WOA algorithm, the hyperparameters of the neural network are determined automatically without the intervention of human agent. In this paper, UNSW-NB15 dataset is used for training and testing. In this research, the PSO-WOA algorithm has use optimized the hyperparameters of the neural network by limiting the search space, and the CNN-LSTM neural network has been trained with this the determined hyperparameters. The results of the implementation indicate that in addition to automating the determination of hyperparameters of the neural network, the detection rate of are method improve 98.5, which is a good improvement compared to other methods.
Z. Maede, M. A. Teixeira, G. Lav, K. M. Khan and J. Raj, "Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things," IEEE Internet of Things Jouranl, pp. 6822 - 6834, 2019.
A.-H. Muna, N. Moustafa and E. Sitnikova, "Identification of malicious activities in industrial internet of things based on deep learning models," Journal of Information Security and Applications, vol. 41, pp. 1-11, 2018.
B. Rafael Ramos Regis, "Anomaly detection in SCADA systems: a network based approach," Centre for Telematics and Information Technology, University of Twente, PhD Thesis, 2014.
P. Dimitrios, S. Panagiotis, L. Thomas and A. G. Sarigiannidis, "A Survey on SCADA Systems; Secure Protocols, Incidents, Threats and Tactics," IEEE Communications Surveys & Tutorials, pp. 1942 - 1976, 2020.
L. Hung-jen, R. L. Chun-Hung, L. Ying-Chih and T. Kuang-Yuan, "Intrusion detection system: A comprehensive review," Journal of Network and Computer Applications, vol. 36, no. 1, pp. 16-24, 2013.
S. G. Farhad and G. Hojjat, "A comprehensive survey: Whale Optimization Algorithm and its applications," Swarm and Evolutionary Computation, vol. 48, pp. 1-24, 2019.
H. Turabieh, M. Mafarja and X. Li, "Iterated feature selection algorithms with layered recurrent neural network for software fault prediction," Expert Systems with Applications, vol. 122, pp. 27-42, 2019.
M. L. Naushad, G. Koushik, C. Indronil, C. Saurav, L. B. Krishna and K. P. Prashanta, "HWPSO: A new hybrid whale-particle swarm optimization algorithm and its application in electronic design optimization problems," Applied Intelligence, p. 265–291, 2019.
A. Sadiqui, Computer Network Security, Britain and United States: WILEY, 2020.
S. Anam, A. Haider and S. Kashif, "Cloud-Assisted IoT-Based SCADA Systems Security: A Review of the State of the Art and Future Challenges," IEEE Access, vol. 4, pp. 1375 - 1384, 2016.
O. Campesato, Artificial Intelligence Machine Learning And Deep Learning, David Pallai, 2020.
E. Min, J. Long, Q. Liu, J. Cui and W. Chen, "TR-IDS: Anomaly-Based Intrusion Detection through Text-Convolutional Neural Network and Random Forest," Hindawi, Security and Communication Network, 2018.
K. Tae-Young and C. Sung-Bae, "Web traffic anomaly detection using C-LSTM neural networks," Expert System With Applications, vol. 106, pp. 66-76, 2018.
G. D. L. T. Parra, R. Rad, K.-K. R. Choo and N. Beebe, "Detecting Internet of Things Attacks using Distributed Deep Learning," Journal of Network and Computer Applications, vol. 163, p. 102662, 2020.
Z. Guangzhen, Z. Cuixiao and Z. Lijuan, "Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network," in 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Guangzhou, China, 2017.
H. Yang, L. Cheng and M. C. Chuah, "Deep-Learning-Based Network Intrusion Detection for SCADA Systems," in 2019 IEEE Conference on Communications and Network Security (CNS), Washington DC, DC, USA, USA, 2019.
Y. Chuanlong, Z. Yuefei, F. Jinlong and H. Xinzheng, "A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks," IEEE Access, vol. 5, pp. 21954 - 21961, 2017.
T. Tuan A, M. Lotfi, M. Des, Z. Syed Ali Raza and G. Mounir, "Deep learning approach for Network Intrusion Detection in Software Defined Networking," in 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, Morocco, 2016.
R. Vinayakumar, A. Mamoun, K. P. Soman, P. Prabaharan, A.-N. Ameer and V. Sitalakshmi, "Deep Learning Approach for Intelligent Intrusion Detection System," IEEE Access, vol. 7, pp. 41525 - 41550, 2019.
N. Mostafa, "Designing an online and reliable statistical anomaly detection framework for dealing with large high-speed network traffic," University of New South Wales, Canberra, Australia, 2017, 2017.
H. Zil E., L. Shahid, A. Jawad, L. Zeba, I. Anas, Z. Zhuo, A. Fehaid and B. Fatmah, "A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things," Journals & Magazines, vol. 9, pp. 55595 - 55605, 2021.
K. James and E. Russell, "Particle swarm optimization," in Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, 1995.
M. Seyedali and L. Andrew, "The Whale Optimization Algorithm," Advances in Engineering Software, vol. 95, pp. 51-67, 2016.
A.-G. Mohammad Ali, M. amr, A.-A. Abdulla khalid, D. Xiaojiang, A. lhsan and G. Mohsen, "A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security," IEEE Communications Serveys & Tutorials, vol. 22, no. 3, pp. 1646 - 1685, 2020.
F. lhab and c. Xu, Data Cleaning, Association for Computing Machinery, 2019.
J. Edward, A User's Guide To Principal Components, New York: John Wiley & Sons, 2005.
C. Alfredo, P. Adam and C. Eugenio, "An Analysis of Deep Neural Network Models for Practical Applications," in Computer Vision and Pattern Recognition (cs.CV), 2017.
