Comparative Analysis of DNN, GBT, and KNN Models for Network Intrusion Detection
Main Article Content
Abstract
Network intrusion detection is critical to cybersecurity, aiming to identify and mitigate unauthorized access and attacks on computer systems and networks. This study evaluates the effectiveness of three machine learning techniques—deep neural networks (DNN), gradient boost trees (GBT), and k-nearest neighbors (KNN)—in detecting network intrusions. The performance of these models was assessed using a comprehensive dataset of 2,540,047 records encompassing 49 features across nine attack categories. The results indicate that GBT outperforms DNN and KNN in accuracy and robustness. These findings highlight the potential of GBT for enhancing intrusion detection systems and contribute valuable insights into the comparative performance of different machine learning algorithms in cybersecurity applications.
Article Details
![Creative Commons License](http://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Avital, M.; Bittencourt, L. F.; Santos, J. L.; Marín, D. B.; Rojas, L. C. Global DDoS Threat Landscape Report 2019.
Axelsson, S. The Base-Rate Fallacy and the Difficulty of Intrusion Detection. ACM Trans. Inf. Syst. Secur. 2000, 3(3), 186-205. https://doi.org/10.1145/357830.357849
Tavallaee, M.; Bagheri, E.; Lu, W.; Ghorbani, A. A. A Detailed Analysis of the KDD CUP 99 Data Set. Proc. 2009 IEEE Symp. Comput. Intell. Secur. Def. Appl., 2009, 1-6. https://doi.org/10.1109/CISDA.2009.5356528
Creech, G.; Hu, J.; Williams, R. A Survey and Comparison of Distributed Intrusion Detection System. J. Netw. Comput. Appl. 2014, 40, 12-33.
Nassar, M.; Alazab, M.; Venkatraman, S. Anomaly Intrusion Detection System: A Comprehensive Review. IEEE Access 2019, 7, 166152-166188.
Garcia, S.; Grill, M.; Stiborek, J.; Zunino, A. An Empirical Comparison of Botnet Detection Methods. Comput. Secur. 2014, 45, 100-123. https://doi.org/10.1016/j.cose.2014.05.011
Moustafa, N.; Slay, J. UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 Network Data Set). In Proceedings of the Military Communications and Information Systems Conference (MilCIS), 2015; IEEE: 2015. https://doi.org/10.1109/MilCIS.2015.7348942
Singh, M.; Kaur, M.; Singh, S. Intrusion Detection System Using Deep Learning Techniques: A Review. 2019.
Yang, X.; Wu, M.; Wei, W.; Li, L. A Boosted Trees Algorithm for Network Intrusion Detection. 2019.
Zhang, Z.; Luo, L.; Zhang, L.; Zhang, Y. Network Intrusion Detection System Based on k-Nearest Neighbor Algorithm. 2019.
Tukey, J. W. Exploratory Data Analysis; 1977.
Ashok, K.; Kumar, S.; Kumari, A.; Saini, A. Edge Computing Based IDS Detecting Threats Using Machine Learning and PyCaret. 2023, DOI: 10.1109/CISES58720.2023.10183591. https://doi.org/10.1109/CISES58720.2023.10183591
Mitchell, T. M. Machine Learning; McGraw Hill: 1997.
Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Springer Science & Business Media: 2009. https://doi.org/10.1007/978-0-387-84858-7
Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; MIT Press: 2016.
Krizhevsky, A.; Sutskever, I.; Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, 2012; pp 1097-1105.
LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Krizhevsky, A.; Sutskever, I.; Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, 2012; pp 1097-1105.
LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521 (7553), 436-444. https://doi.org/10.1038/nature14539
Duangklang, P.; Kruakaew, R. Models for Automatic Aircraft Type Prediction. NKRAFA J. Sci. Technol. 2019.
Confusion Matrix - An Overview. Available online: https://www.sciencedirect.com/topics/engineering/ confusion-matrix (accessed on June 8, 2022).
Using Machine Learning Techniques Random Forest and Neural Network to Detect Cyber Attacks. 2023, DOI: 10.14293/pr2199.000059.v1. https://doi.org/10.14293/PR2199.000059.v1
Patrick, S. Machine Learning Based Network Attacks Classification. 2023, DOI: 10.1109/icpeca56706. 2023.10075818.
Jingwen, W.; Peilong, L. MalIoT: Scalable and Real-time Malware Traffic Detection for IoT Networks. 2023, arXiv:2304.00623.
Michael, G. A Machine-Learning Procedure to Detect Network Attacks. J. Complex Netw. 2023, DOI: 10.1093/comnet/cnad017. https://doi.org/10.1093/comnet/cnad017
Ashok, K.; Kumar, S.; Kumari, A.; Saini, A. Edge Computing Based IDS Detecting Threats Using Machine Learning and PyCaret. 2023, DOI: 10.1109/CISES58720.2023.10183591. https://doi.org/10.1109/CISES58720.2023.10183591
Fuat, T. Analysis of Intrusion Detection Systems in UNSW-NB15 and NSL-KDD Datasets with Machine Learning Algorithms. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 2023, DOI: 10.17798/bitlisfen.1240469. https://doi.org/10.17798/bitlisfen.1240469
Aleesa, A.; Thanoun, M.; Mohammed, A.; Sahar, N. DEEP-INTRUSION DETECTION SYSTEM WITH ENHANCED UNSW-NB15 DATASET BASED ON DEEP LEARNING TECHNIQUES. J. Eng. Sci. Technol. 2021, 16, 711-727.
Zhou, Y.; Han, M.; Liu, L.; He, J.S.; Wang, Y. Deep Learning Approach for Cyberattack Detection. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS); IEEE: 2018, pp 262-267. https://doi.org/10.1109/INFCOMW.2018.8407032
Faker, O.; Dogdu, E. Intrusion Detection Using Big Data and Deep Learning Techniques. ACM: 2019, DOI: 10.1145/3299815.3314439. https://doi.org/10.1145/3299815.3314439
Kasongo, S. M.; Sun, Y. Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset. J. Big Data 2020, 7, 105, DOI: 10.1186/s40537-020-00379-6. https://doi.org/10.1186/s40537-020-00379-6
Rahman, O.; Quraishi, M. A. G. Experimental Analysis of Random Forest, K-Nearest Neighbor and Support Vector Machine Anomaly Detection. 2019, DOI: 10.13140/RG.2.2.19998.18245. [33]McKinney, W. Pandas, Python Data Analysis Library. 2015.
Oliphant, T. E. Guide to Numpy; Trelgol Publishing: 2006, 1, 85.
Khandare, A.; Agarwal, N.; Bodhankar, A.; Kulkarni, A.; Mane, I. Analysis of Python Libraries for Artificial Intelligence. In Intelligent Computing and Networking: Proceedings of IC-ICN 2022; Springer Nature Singapore: Singapore, 2023, pp 157-177. https://doi.org/10.1007/978-981-99-0071-8_13
Ari, N.; Ustazhanov, M. Matplotlib in Python. In 2014 11th International Conference on Electronics, Computer and Computation (ICECCO); IEEE: 2014, pp 1-6. https://doi.org/10.1109/ICECCO.2014.6997585
Sundaram, J.; Gowri, K.; Devaraju, S.; Gokuldev, S.; Jayaprakash, S.; Anandaram, H.; Thenmozhi, M. An Exploration of Python Libraries in Machine Learning Models for Data Science. In Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science; IGI Global: 2023, pp 1-31. https://doi.org/10.4018/978-1-6684-8696-2.ch001