Research Article |
An Approach for Identifying Network Intrusion in an Automated Process Control Computer System
Author(s): Abhijit Das1 and Pramod2
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 4, SI on Applications of AI and IOT Process Control
Publisher : FOREX Publication
Published : 25 December 2022
e-ISSN : 2347-470X
Page(s) : 1219-1224
Abstract
Technology and networks have improved significantly in recent decades, and Internet services are now available in almost every business. It has become increasingly important to develop information security technology to identify the most recent attack as hackers are getting better at stealing information. The most important technology for security is an Intrusion Detection System (IDS) which employs machine learning and deep learning technique to identify network irregularities. To detect an unknown attack, we propose to use a new intrusion detection system using a deep neural network methodology which provides excellent performance to detect intrusion. This research focuses on an automated process control computer system that recognizes, records, analyzes, and correlates threats to online safety. In addition, two different methods are used to detect an attack (the binary classification and the multiclass classification). One of the most promising features of the proposed technique is its accuracy (98.99 percent with the multiclass classification and the binary classification). The proposed method's first step creates a model for a multiclass intrusion detection system based on CNN. FOA (Fruit Fly Optimization Algorithm) is used in the process's pre-training phase to address the class imbalance issue. Each batch is obtained during the training process using the resampling method following the resampling weights, which are the results of the pre-training procedure.
Keywords: Intrusion Detection System
, ensemble approach
, cyber-attacks
, CNN
, Automated Process Control
, FOA
.
Abhijit Das*, Research Scholar, Department of CSE, VTU, PESITM, Shimoga-577205, Karnataka, India; Email: abhijit.tec@gmail.com
Pramod, Associate Professor, Department of ISE, PESITM, VTU, Shimoga-577205, Karnataka, India; Email: pramod741230@gmail.com
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[1] L.; Quan, Y. Dynamic Enabling Cyberspace Defense; People’s Posts and Telecommunications Press: Beijing, China, 2018.[Cross Ref]
-
[2] Ren, X.K.; Jiao, W.B.; Zhou, D. Intrusion Detection Model of Weighted Navie Bayes Based on Particle Swarm Optimization Algorithm. Comput. Eng. Appl. 2016, 52, 122–126.[Cross Ref]
-
[3] Teng, L.; Teng, S.; Tang, F.; Zhu, H.; Zhang, W.; Liu, D.; Liang, L. A Collaborative and Adaptive Intrusion Detection Based on SVMs and Decision Trees. In Proceedings of the IEEE International Conference on Data Mining Workshop, Shenzhen, China, 14 December 2014; pp. 898–905. [Cross Ref]
-
[4] Chen, S.X.; Peng, M.L.; Xiong, H.L.; Yu, X. SVM Intrusion Detection Model Based on Compressed Sampling. J. Electr. Comput. Eng. 2016, 2016, 6. [Cross Ref]
-
[5] Reddy, R.R.; Ramadevi, Y.; Sunitha, K.V.N. Effective discriminant function for intrusion detection using SVM. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 21–24 September 2016; pp. 1148–1153.[Cross Ref]
-
[6] Tao, Z.; Sun, Z. An Improved Intrusion Detection Algorithm Based on GA and SVM. IEEE Access 2018, 6, 13624–13631. [Cross Ref]
-
[7] Wang, H.W.; Gu, J.; Wang, S.S. An Effective Intrusion Detection Framework Based on SVM with Feature Augmentation. Knowl.-Based Syst. 2017, 136, 130–139. [Cross Ref]
-
[8] Sahu, S.K.; Katiyar, A.; Kumari, K.M.; Kumar, G.; Mohapatra, D.P. An SVM-Based Ensemble Approach for Intrusion Detection. Int. J. Inf. Technol. Web Eng. 2019, 14, 66–84.[Cross Ref]
-
[9] Sahu, S.; Mehtre, B.M. Network intrusion detection system using J48 Decision Tree. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, India, 10–13 August 2015; pp. 2023–2026.[Cross Ref]
-
[10] Jiang, F.; Chun, C.P.; Zeng, H.F. Relative Decision Entropy Based Decision Tree Algorithm and Its Application in Intrusion Detection. Comput. Sci. 2012, 39, 223–226.[Cross Ref]
-
[11] Ahmim, A.; Maglaras, L.A.; Ferrag, M.A.; Derdour, M.; Janicke, H. A Novel Hierarchical Intrusion Detection System Based on Decision Tree and Rules-Based Models. In Proceedings of the 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece, 29–31 May 2019; pp. 228–233. [Cross Ref]
-
[12] Yun, W. A Multinomial Logistic Regression Modeling Approach for Anomaly Intrusion Detection. Comput. Secur. 2005, 24, 662–674 [Cross Ref]
-
[13] Kamarudin, M.H.; Maple, C.; Watson, T.; Sofian, H. Packet Header Intrusion Detection with Binary Logistic Regression Approach in Detecting R2L and U2R Attacks. In Proceedings of the Fourth International Conference on Cyber Security, Cyber Warfare, and Digital Forensic (CyberSec), Jakarta, Indonesia, 29–31 October 2015; pp. 101–106. [Cross Ref]
-
[14] Kumar, Gulshan. "Evaluation metrics for intrusion detection systems-a study." Evaluation 2.11 (2014): 11-7.[Cross Ref]
-
[15] LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521, 436–444.[Cross Ref]
Abhijit Das and Pramod (2022), An Approach for Identifying Network Intrusion in an Automated Process Control Computer System. IJEER 10(4), 1219-1224. DOI: 10.37391/IJEER.100472.