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Anomaly Detection System in the Industrial Internet of Things Network with Convolutional Neural Network | ||
| Computational Sciences and Engineering | ||
| مقاله 7، دوره 3، شماره 2، آذر 2023، صفحه 253-263 اصل مقاله (511.41 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22124/cse.2024.26046.1069 | ||
| نویسندگان | ||
| Rahim Asghari* 1؛ Sajjad Ghasemzadeh2؛ Mohammad Allahyari3 | ||
| 1Department of mathematics, Technical and Vocational University, Tehran, Iran. | ||
| 2University of Tehran, College of Engineering, Tehran, Iran. | ||
| 3Urmia University of Technology, Urmia, Iran. | ||
| چکیده | ||
| Today, information security and preventing data theft are of great importance in the industrial Internet of Things. For this reason, in order to improve the network, it is necessary to use a suitable intrusion detection system to detect anomalies and improve the network. Machine learning is one of the powerful methods used for network modeling and diagnosis. In this article, with the help of convolutional neural network modeling, which is considered one of the powerful methods of machine learning, an intrusion detection system with optimal performance in abnormal traffic detection is presented. In this method, the proposed model is implemented and shown in several classes. Also, data processing to NSL-KDD datasets is applied in this paper to obtain appropriate results that indicate the appropriate quality of the proposed evaluation model; Therefore, according to the simulation results, the accuracy and true positive rate of the NSL-KDD data set, and the proposed neural network model, the accuracy and true positive rate on the NSL-KDD data set have reached 92.3% and 88.5%, respectively. | ||
| کلیدواژه‌ها | ||
| Industrial Internet of Things؛ Network security؛ Intrusion detection system؛ Deep learning؛ Convolutional neural network | ||
| مراجع | ||
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[1] Hasan, M., et al. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 7. [2] Asghari, R., & Semyari, R. (2022). A Mutual Lightweight Authentication Protocol for Internet of Things Environment Using smart card. Computational Sciences and Engineering, 2(1), 41-52. [3] Jiang, J., et al. (2022). A dynamic ensemble algorithm for anomaly detection in IoT imbalanced data streams. Computer Communications, 194, 250-257. [4] Ever, Y. K., B. Sekeroglu, B., & Dimililer, K. (2019). Classification analysis of intrusion detection on NSL-KDD using machine learning algorithms. International Conference on Mobile Web and Intelligent Information Systems, Springer. [5] Asghari, R. (2021). A modified continuous lightweight authentication to increase the information security on internet of Things. Computational Sciences and Engineering, 1(2), 109-121. [6] Muniyandi, A.P., Rajeswari, R., & Rajaram, R. (2012). Network anomaly detection by cascading k-Means clustering and C4. 5 decision tree algorithm. Procedia Engineering, 30, 174-182. [7] Tobiyama, S., et al. (2016). Malware detection with deep neural network using process behavior. IEEE 40th annual computer software and applications conference (COMPSAC). [8] Li, F., et al. (2014). Event-centric situation trust data aggregation mechanism in distributed wireless network. International Journal of Distributed Sensor Networks, 10(8), 585302. [9] Masoodi, F. (2021). Machine Learning for Classification analysis of Intrusion Detection on NSL-KDD Dataset. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 12(10), 2286-2293. [10] Ingre, B., Yadav, A., & Soni, A.K. (2017). Decision tree based intrusion detection system for NSL-KDD dataset. International conference on information and communication technology for intelligent systems. Springer. [11] Su, T., et al., (2020). BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access, 8, 29575-29585. [12] Pourghaffari, A., Asghari, R., & Rashidi, A.J. (2022). Toward Cyber Command and Control System Architecture Using Data-Driven Analysis Solutions. Computational Sciences and Engineering, 2(1), 53-68. [13] Pascanu, R., et al. (2015). Malware classification with recurrent networks. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). | ||
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