International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 17, No. 1, 8 Feb. 2025
Cover page and Table of Contents: PDF (size: 1006KB)
Modified CNN Model for Network Intrusion Detection and Classification System Using Local Outlier Factor-based Recursive Feature Elimination
PDF (1006KB), PP.82-91
Views: 0 Downloads: 0
Author(s)
Index Terms
Network Intrusion Detection and Classification, Convolutional Neural Network, Recursive Feature Elimination, Local Outlier factor, Denial-of-service
Abstract
An intrusion detection system (IDS) is either a part of a software or hardware environment that monitors data and analyses it to identify any attacks made against a system or a network. Traditional IDS approaches make the system more complicated and less efficient, because the analytical properties process is difficult and time-consuming. This is because the procedure is complex. Therefore, this research work focuses on a network intrusion detection and classification (NIDCS) system using a modified convolutional neural network (MCNN) with recursive feature elimination (RFE). Initially, the dataset is balanced with the help of the local outlier factor (LOF), which finds anomalies and outliers by comparing the amount of deviation that a single data point has with the amount of deviation that its neighbors have. Then, a feature extraction selection approach named RFE is applied to eliminate the weakest features until the desired number of features is achieved. Finally, the optimal features are trained with the MCNN classifier, which classifies intrusions like probe, denial-of-service (DoS), remote-to-user (R2U), user-to-root (U2R), and identifies normal data. The proposed NIDCS system resulted in higher performance with 99.3% accuracy and a 3.02 false alarm rate (FAR) as equated to state-of-the-art NIDCS approaches such as deep neural networks (DNN), ResNet, and gravitational search algorithms (GSA).
Cite This Paper
Kondru Mounika, P. Venkateswara Rao, Anand Anbalagan, "Modified CNN Model for Network Intrusion Detection and Classification System Using Local Outlier Factor-based Recursive Feature Elimination", International Journal of Computer Network and Information Security(IJCNIS), Vol.17, No.1, pp.82-91, 2025. DOI:10.5815/ijcnis.2025.01.07
Reference
[1]M. Chalé, and N. D. Bastian. "Generating realistic cyber data for training and evaluating machine learning classifiers for network intrusion detection systems." Expert Systems with Applications 207 (2022): 117936.
[2]G. Apruzzese, M. Andreolini, L. Ferretti, M. Marchetti, and M. Colajanni, (2022). Modeling realistic adversarial attacks against network intrusion detection systems. Digital Threats: Research and Practice (DTRAP), 3(3), 1-19.
[3]Apruzzese, G., Pajola, L., & Conti, M. (2022). The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems. IEEE Transactions on Network and Service Management.
[4]Zhang, C., Costa-Pérez, X., & Patras, P. (2022). Adversarial Attacks Against Deep Learning-Based Network Intrusion Detection Systems and Defense Mechanisms. IEEE/ACM Transactions on Networking.
[5]Asad, Hafizul, and Ilir Gashi. "Dynamical analysis of diversity in rule-based open source network intrusion detection systems." Empirical Software Engineering 27.1 (2022): 1-30.
[6]Sheatsley, R., Papernot, N., Weisman, M. J., Verma, G., & McDaniel, P. (2022). Adversarial examples for network intrusion detection systems. Journal of Computer Security, (Preprint), 1-26.
[7]Fu, Y., Du, Y., Cao, Z., Li, Q., & Xiang, W. (2022). A Deep Learning Model for Network Intrusion Detection with Imbalanced Data. Electronics, 11(6), 898.
[8]Magan-Carrion, R., Urda, D., Diaz-Cano, I., & Dorronsoro, B. (2022). Improving the Reliability of Network Intrusion Detection Systems through Dataset Aggregation. IEEE Transactions on Emerging Topics in Computing.
[9]Data, M., & Aritsugi, M. (2022, July). AB-HT: An Ensemble Incremental Learning Algorithm for Network Intrusion Detection Systems. In 2022 International Conference on Data Science and Its Applications (ICoDSA) (pp. 47-52). IEEE.
[10]Antunes, M., Oliveira, L., Seguro, A., Veríssimo, J., Salgado, R., & Murteira, T. (2022, March). Benchmarking Deep Learning Methods for Behaviour-Based Network Intrusion Detection. In Informatics (Vol. 9, No. 1, p. 29). MDPI.
[11]Moizuddin, M. D., & Jose, M. V. (2022). A bio-inspired hybrid deep learning model for network intrusion detection. Knowledge-Based Systems, 238, 107894.
[12]Sarhan, M., Layeghy, S., & Portmann, M. (2022). Towards a standard feature set for network intrusion detection system datasets. Mobile Networks and Applications, 27(1), 357-370.
[13]Habeeb, Mohammed Sayeeduddin, and T. Ranga Babu. "Network intrusion detection system: A survey on artificial intelligence‐based techniques." Expert Systems (2022): e13066.
[14]Hindy, Hanan and Brosset, David and Bayne, Ethan and Seeam, Amar and Tachtatzis, Christos and Atkinson, Robert and Bellekens, Xavier (2018) A taxonomy and survey of intrusion detection system design techniques, network threats and datasets.
[15]Salih, Azar Abid, and Adnan Mohsin Abdulazeez. "Evaluation of classification algorithms for intrusion detection system: A review." Journal of Soft Computing and Data Mining 2.1 (2021): 31-40.
[16]Dang, Quang-Vinh. "Active Learning for Intrusion Detection Systems." IEEE Research, Innovation and Vision for the Future. 2020.
[17]Li, Jieling, et al. "Semi-supervised machine learning framework for network intrusion detection." The Journal of Supercomputing (2022): 1-23.
[18]Al-Safi, Ali Hussein Shamman, Zaid Ibrahim Rasool Hani, and Musaddak M. Abdul Zahra. "Using a hybrid algorithm and feature selection for network anomaly intrusion detection." J. Mech. Eng. Res. Dev 44 (2021): 253-262.
[19]Gopalakrishnan, B., and P. Purusothaman. "A new design of intrusion detection in IoT sector using optimal feature selection and high ranking-based ensemble learning model." Peer-to-Peer Networking and Applications 15.5 (2022): 2199-2226.
[20]Panigrahi, R., Borah, S., Pramanik, M., Bhoi, A. K., Barsocchi, P., Nayak, S. R., & Alnumay, W. (2022). Intrusion detection in cyber–physical environment using hybrid Naïve Bayes—Decision table and multi-objective evolutionary feature selection. Computer Communications, 188, 133-144.
[21]Rao, K. Narayana, K. Venkata Rao, and Prasad Reddy PVGD. "A hybrid intrusion detection system based on sparse autoencoder and deep neural network." Computer Communications 180 (2021): 77-88.
[22]Tait, Kathryn-Ann, et al. "Intrusion detection using machine learning techniques: an experimental comparison." 2021 International Congress of Advanced Technology and Engineering (ICOTEN). IEEE, 2021.
[23]Keserwani, Pankaj Kumar, et al. "A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO–PSO–RF model." Journal of Reliable Intelligent Environments 7.1 (2021): 3-21.
[24]Mishra, Preeti, et al. "A detailed investigation and analysis of using machine learning techniques for intrusion detection." IEEE communications surveys & tutorials 21.1 (2018): 686-728.
[25]Rodda, Sireesha. "Network intrusion detection systems using neural networks." Information systems design and intelligent applications. Springer, Singapore, 2018. 903-908.
[26]Xiao, Yuelei, and Xing Xiao. "An intrusion detection system based on a simplified residual network." Information 10.11 (2019): 356.
[27]Rahman, Md Arafatur, et al. "Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection." Multimedia Tools and Applications 80.20 (2021): 31381-31399.
[28]Al-Duwairi, Basheer, et al. "SIEM-based detection and mitigation of IoT-botnet DDoS attacks." International Journal of Electrical and Computer Engineering 10.2 (2020): 2182.
[29]Krishnaveni, Sivamohan, et al. "Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing." Cluster Computing 24.3 (2021): 1761-1779.z
[30]Raj, Meghana G., and Santosh Kumar Pani. "A meta-analytic review of intelligent intrusion detection techniques in cloud computing environment." International Journal of Advanced Computer Science and Applications 12.10 (2021).
[31]K. Mounika and P. V. Rao, "IDCSNet: Intrusion Detection and Classification System using Unified Gradient-Boosted Decision Tree Classifier," International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, 2022, pp. 1159-1164.