Design and Implementation of Machine Learning-Based Network Intrusion Detection
Keywords:
Network intrusion detection, Cyber security, Support vector machine, ensemble learningAbstract
Systems for detecting intrusions are vital to network security and are necessary for maintaining network integrity. To improve the effectiveness of machine learning techniques, ensemble learning has been frequently used. Additionally, the quality of training data has a significant impact on detecting abilities. Marginal density ratios have consistently outperformed the powerful unilabiate classifiers. In this paper, we recommend a system for detecting intrusions that is based on groups of SVMs and has been functionally enhanced. Our method involves transforming the original characteristics to marginal density logarithmic thresholds in order to produce new, enhanced, and modified training data. The creation of an intrusion detection model then takes place using an SVM-Set. The system employs a number of machine learning techniques, including ensemble learning, to enhance detection performance. Raising the caliber of training data also involves the use of feature augmentation. Using an ensemble of Support Vector Machine (SVM) models, the suggested approach creates an effective intrusion detection framework. The effectiveness of the proposed design was assessed using simulations and the data base CICIDS2017, which simulates network traffic in the real world. The results of the experiment were compared to earlier studies, and it was found that the precision of binary and multiclass categorization had increased. Another illustration of the efficiency of the model was the high level of precision of the restored transportation system.
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A. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153–1176, 2017.
N. Farnaaz and M. A. Jabbar, “Random forest modeling for network intrusion detection system,” Procedia Computer Science, vol. 89, pp. 213–217, 2016.
H. Wang, J. Gu, and S. Wang, “An effective intrusion detection framework based on SVM with feature augmentation,” Knowledge-Based Systems, vol. 136, pp. 130–139, 2017.
P. S. Bhattacharjee, A. K. M. Fujail, and S. A. Begum, “A comparison of intrusion detection by K-means and fuzzy C-means clustering algorithm over the NSL-KDD dataset,” in Proceedings of the 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), IEEE, Chennai, India, 2017.
I. M. Akashdeep, I. Manzoor, and N. Kumar, “A feature reduced intrusion detection system using ann classifier,” Expert Systems with Applications, vol. 88, pp. 249–257, 2017.
Y. Chuan-Long, Z. Yue-Fei, F. Jin-Long et al., “A deep learning approach for intrusion detection using recurrent neural networks,” IEEE Access, vol. 5, pp. 21954–21961, 2017.
M. Lopez-Martin, B. Carro, and A. Sanchez-Esguevillas, “Application of deep reinforcement learning to intrusion detection for supervised problems,” Expert Systems with Applications, vol. 141, Article ID 112963, 2019.
H. He, X. Sun, H. He, G. Zhao, L. He, and J. Ren, “A novel multimodal-sequential approach based on multi-view features for network intrusion detection,” IEEE Access, vol. 7, pp. 183207–183221, 2019.
P. Sun, P. Liu, Q. Li et al., “DL-IDS: extracting features using CNN-LSTM hybrid network for intrusion detection system,” Security and Communication Networks, vol. 2020, Article ID 8890306, 11 pages, 2020.
R. M. Elbasiony, E. A. Sallam, T. E. Eltobely, and M. M. Fahmy, “A hybrid network intrusion detection framework based on random forests and weighted k-means,” Ain Shams Engineering Journal, vol. 4, no. 4, pp. 753–762, 2013.
W. Wang, T. Guyet, R. Quiniou, M.-O. Cordier, F. Masseglia, and X. Zhang, “Autonomic intrusion detection: adaptively detecting anomalies over unlabeled audit data streams in computer networks,” Knowledge-Based Systems, vol. 70, pp. 103–117, 2014.
H. Yao, Q. Wang, L. Wang, P. Zhang, M. Li, and Y. Liu, “An intrusion detection framework based on hybrid multi-level data mining,” International Journal of Parallel Programming, vol. 47, no. 4, pp. 740–758, 2019.
H. Zhang, Y. Li, Z. Lv, A. K. Sangaiah, and T. Huang, “A real-time and ubiquitous network attack detection based on deep belief network and support vector machine,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 3, pp. 790–799, 2020.
E. Min, J. Long, Q. Liu et al., “Su-IDS: a semi-supervised and unsupervised framework for network intrusion detection,” in Proceedings of the International Conference on Cloud Computing and Security, pp. 322–334, Springer, Cham, Switzerland, 2018.
M. R. Karbir, R. Onik, and T. Samad, “A network intrusion detection framework based on bayesian network using wrapper approach,” International Journal of Computer Applications, vol. 166, no. 4, pp. 975–8887, 2017.
J. Gu, L. Wang, H. Wang, and S. Wang, “A novel approach to intrusion detection using SVM ensemble with feature augmentation,” Computers & Security, vol. 86, pp. 53–62, 2019.
A. Ahmim, L. Maglaras, M. A. Ferrag et al., “A novel hierarchical intrusion detection system based on decision tree and rules-based models,” in 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 228–223, IEEE, Santorini, Greece, 2019.
V. Kumar, V. Choudhary, V. Sahrawat et al., “Detecting intrusions and attacks in the network traffic using anomaly based techniques,” in Proceedings of the 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 554–560, IEEE, Coimbatore, India, 2020.
A. S. Qureshi, A. Khan, N. Shamim, and M. H. Durad, “Intrusion detection using deep sparse auto-encoder and self-taught learning,” Neural Computing and Applications, vol. 32, no. 8, pp. 3135–3147, 2020.
S. Nathan, T. N. Ngoc, V. D. Phai et al., “A deep learning approach to network intrusion detection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41–50, 2018.
A. Y. Javaid, Q. Niyaz, W. Sun et al., “A deep learning approach for network intrusion detection system,” in Proceedings of the 9th Eai International Conference on Bio-inspired Information & Communications Technologies, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 21–26, Nairobi, Kenya, 2016.
M. Al-Qatf, Y. Lasheng, M. Al-Habib, and K. Al-Sabahi, “Deep learning approach combining sparse autoencoder with svm for network intrusion detection,” IEEE Access, vol. 6, pp. 52843–52856, 2018.
R. Abdulhammed, H. Musafer, A. Alessa et al., “Features dimensionality reduction approaches for machine learning based network intrusion detection,” Electronics, vol. 8, no. 3, 2019.
G. C. Fernández and S. Xu, “A case study on using deep learning for network intrusion detection,” in Proceedings of the MILCOM 2019-2019 IEEE Military Communications Conference (MILCOM), pp. 1–6, IEEE, Norfolk, VA, USA, 2019.
C. Yin, Y. Zhu, S. Liu, J. Fei, and H. Zhang, “Enhancing network intrusion detection classifiers using supervised adversarial training,” The Journal of Supercomputing, vol. 76, no. 9, pp. 6690–6719, 2020.
P. Madani and N. Vlajic, “Robustness of deep autoencoder in intrusion detection under adversarial contamination,” in Proceedings of the 5th Annual Symposium and Bootcamp, pp. 1–8, ACM, New York, NY, USA, 2018.
E. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units(elus),” 2016
S. Ustebay, Z. Turgut, and M. A. Aydin, “Cyber attack detection by using neural network approaches: shallow neural network, deep neural network and autoencoder,” in Proceedings of the 2019 International Conference on Computer Networks, pp. 144–155, Spinger, Cham, Switzerland, 2019.
L. Breiman, J. Friedman, J. Charles et al., Classification and Regression Trees, Chapman and Hall/CRC, London, UK, 1984.
Dataset CICIDS2017: https://www.kaggle.com/datasets/cicdataset/cicids2017/code
M. Tavallaee, E. Bagheri, W. Lu et al., “A detailed Analysis of the KDD CUP 99 data set,” in Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6, IEEE, Ottawa, Canada, 2009.
I. Sharafaldin and A. Ali, “Ghorbani toward generating a new intrusion detection dataset and intrusion traffic characterization,” in Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP), pp. 108–116, Funchal, Portugal, 2018.
M. Patel, B. Naughton, C. Chan et al., “Mobile-edge computing introductory technical white paper: mobile-edge computing(MEC) industry initiative,” 2014.
M. Eskandari, Z. H. Janjua, M. Vecchio et al., “Passban IDS: an intelligent anomaly-based intrusion detection system for IoT Edge devices,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6882–6897, 2020.
Y. Zhang, W. Jia, and K. Wang, “An edge IDS based on biological immune principles for dynamic threat detection,” Wireless Communications and Mobile Computing, vol. 2020, Article ID 8811035, 15 pages, 2020.
P. A. A. Resende and A. C. Drummond, “A survey of random forest based Methods for intrusion detection systems,” ACM Computing Surveys, vol. 51, no. 3, pp. 1–36, 2018.
Khetani, V. ., Gandhi, Y. ., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262.
S. Raponi, M. Caprolu, and R. Di Pietro, “Intrusion detection at the network edge: solutions, limitations, and future directions,” in Proceedings of the International Conference on Edge Computing, Springer, Rome, Italy, 2019.
Vijayalakshmi, V., & Sharmila, K. (2023). Secure Data Transactions based on Hash Coded Starvation Blockchain Security using Padded Ring Signature-ECC for Network of Things. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 53–61. https://doi.org/10.17762/ijritcc.v11i1.5986
Jones, D., Taylor, M., García, L., Rodriguez, A., & Fernández, C. Using Machine Learning to Improve Student Performance in Engineering Programs. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/101
Sherje, N.P., Agrawal, S.A., Umbarkar, A.M., Kharche, P.P., Dhabliya, D. Machinability study and optimization of CNC drilling process parameters for HSLA steel with coated and uncoated drill bit (2021) Materials Today: Proceedings,.
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