Exploring the NSL-KDD Dataset: A Comprehensive Analysis about Intrusion Detection System (IDS)
DOI:
https://doi.org/10.32628/IJSRST251222614Keywords:
IDS, Network Security, NSL-KDD, Machine Learning, XGB Classifier, Logistic RegressionAbstract
In this research article the researcher emphasized the Network threats and hazards are evolving at a high-speed rate in recent years. Many mechanisms (such as firewalls, anti-virus, anti-malware, and spam filters) are being used as security tools to protect networks. An intrusion detection system (IDS) is also an effective and powerful network security system to detect unauthorized and abnormal network traffic flow. This article presents a review of the research trends in network-based intrusion detection systems (NIDS), their approaches, and the most common datasets used to evaluate IDS Models. The analysis reported presented in this paper is based on the supervised machine learning approach logistics and XGB- classifier by using NSL-KDD Dataset. The researcher found that logistic classifier given 0.95% accuracy where as XGBooster Classifier gives the 1.00% accuracy , due to the over fitting the researcher used the hyper parameter tuning XGB classifier and got the 0.99% accuracy. The researcher assured that the developed predictive model is more accurate and efficient to detect the intrusion during the data transmission.
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A. Abbasi, J. Wetzels, W. Bokslag, E. Zambon, and S. Etalle, "On emulation-based network intrusion detection systems," in Research in attacks, intrusions and defenses: 17th international symposium, RAID 2014, Gothenburg, Sweden, September 17–19, 2014. Proceedings, A. Stavrou, H. Bos, and G. Portokalidis, Eds. Cham: Springer International Publishing, 2014, pp. 384–404
A. A. Aburomman and M. B. Ibne Reaz, "A novel SVM-kNN-PSO ensemble method for intrusion detection system," Appl Soft Comput, vol. 38, pp. 360–372, 2016/01/01/ 2016.
Adebowale A, Idowu S, Amarachi AA (2013) Comparative study of selected data mining algorithms used for intrusion detection. International Journal of Soft Computing and Engineering (IJSCE) 3(3):237–241.
Agrawal S, Agrawal J (2015) Survey on anomaly detection using data mining techniques. Procedia Computer Science 60:708–713.
M. Ahmed, A. Naser Mahmood, and J. Hu, "A survey of network anomaly detection techniques," J Netw Comput Appl, vol. 60, pp. 19–31, 1// 2016.
A. Alazab, J. Abawajy, M. Hobbs, R. Layton, and A. Khraisat, "Crime toolkits: the Productisation of cybercrime," in 2013 12th IEEE international conference on trust, security and privacy in computing and communications, 2013, pp. 1626–1632.
A. Alazab, M. Hobbs, J. Abawajy, and M. Alazab, "Using feature selection for intrusion detection system," in 2012 international symposium on communications and information technologies (ISCIT), 2012, pp. 296–301.
S. Zhao, M. Chandrashekar, Y. Lee, and D. Medhi, ―Real-time network anomaly detection system using machine learning,‖ in 2015 11th International Conference on the Design of Reliable Communication Networks (DRCN), Mar. 2015, pp. 267–270. doi: 10.1109/DRCN. 2015.7149025.
W. L. Al-Yaseen, Z. A. Othman, and M. Z. A. Nazri, ―Multi-level hybrid support vector machine and extreme learning machine based on modified Kmeans for intrusion detection system,‖ Expert Syst. Appl., vol. 67, pp. 296– 303, Jan. 2017, doi: 10.1016/j.eswa.2016.09.041.
Y.-X. Meng, ―The practice on using machine learning for network anomaly intrusion detection,‖ in 2011 International Conference on Machine Learning and Cybernetics, Jul. 2011, vol. 2, pp. 576–581. doi: 10.1109/ICMLC.2011.6016798.
A. Tsiligkaridis and I. Ch. Paschalidis, ―Anomaly detection in transportation networks using machine learning techniques,‖ in 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), Nov. 2017, pp. 1–4. doi: 10.1109/URTC.2017.8284194.
M. E. KarsligЕl, A. G. Yavuz, M. A. Güvensan, K. Hanifi, and H. Bank, ―Network intrusion detection using machine learning anomaly detection algorithms,‖ in 2017 25th Signal Processing and Communications.
R. Patgiri, U. Varshney, T. Akutota, and R. Kunde, ―An Investigation on Intrusion Detection System Using Machine Learning,‖ in 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Nov. 2018, pp.1684–1691. doi: 10.1109/SSCI.2018.8628676.
B. S. Bhati, C. S. Rai, B. Balamurugan, and F. Al-Turjman, ―An intrusion detection scheme based on the ensemble of discriminant classifiers,‖ Comput. Electr. Eng., vol. 86, p. 106742, Sep. 2020, doi:10.1016/j.compeleceng.2020.106742.
Shubham Malhotra, Muhammad Saqib, Dipkumar Mehta, and Hassan Tariq. (2023). Efficient Algorithms for Parallel Dynamic Graph Processing: A Study of Techniques and Applications. International Journal of Communication Networks and Information Security (IJCNIS), 15(2), 519–534.
Divyatmika and M. Sreekesh, ―A two-tier network based intrusion detection system architecture using machine learning approach,‖ in 2016 International Conference on Electrical, Electronics, and Optimization Techniques(ICEEOT), Mar. 2016, pp. 42–47. doi: 10.1109/ICEEOT.2016.7755404.
D. Ashok Kumar and S. R. Venugopalan, ―A Novel Algorithm for Network Anomaly Detection Using Adaptive Machine Learning,‖ in Progress in Advanced Computing and Intelligent Engineering, Singapore, 2018, pp. 59–69. doi: 10.1007/978-981-10-6875-1_7.
A Novel Malware Analysis Framework for Malware Detection and Classification using Machine Learning Approach | Proceedings of the 19th International Conference on Distributed Computing and Networking.‖ https://dl.acm.org/doi/abs/10.1145/3154273.3154326 (accessed Aug. 22).
T. Kacem, D. Wijesekera, P. Costa, and A. Barreto, ―An ADS-B Intrusion Detection System,‖ in 2016 IEEE Trustcom/Big Data SE/ISPA, Aug. 2016, pp. 544–551. doi: 10.1109/TrustCom.2016.0108.
M. S. Koli and M. K. Chavan, ―An advanced method for detection of botnet traffic using intrusion detection system,‖ in 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), Mar. 2017, pp. 481–485. doi: 10.1109/ICICCT.2017.7975246.
P. I. Radoglou-Grammatikis and P. G. Sarigiannidis, ―Flow anomaly based intrusion detection system for Android mobile devices,‖ in 2017 6th International Conference on Modern Circuits and Systems Technologies (MOCAST), May 2017, pp. 1–4. doi: 10.1109/MOCAST.2017.7937625.
Yerra, S. (2024). The impact of AI-driven data cleansing on supply chain data accuracy and master data management. Smart Computing Systems, 4(1), 187-191. https://doi.org/10.61485/SMCS.27523829/v4n1P1
K. N. K. Thapa and N. Duraipandian, ―Malicious Traffic classification Using Long Short-Term Memory (LSTM) Model,‖ Wirel. Pers. Commun., vol. 119, no. 3, pp. 2707–2724, Aug. 2021, doi: 10.1007/s11277-021-08359-6.
Sachin Dixit, & Jagdish Jangid. (2024). Asynchronous SCIM Profile for Security Event Tokens. Journal of Computational Analysis and Applications (JoCAAA), 33(06), 1357–1371. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1935
M. Agarwal, S. Purwar, S. Biswas, and S. Nandi, ―Intrusion detection system for PS-Poll DoS attack in 802.11 networks using real time discrete event system,‖ IEEECAA J. Autom. Sin., vol. 4, no. 4, pp. 792–808, 2017, doi:10.1109/JAS.2016.7510178.
N. Moustafa and J. Slay, ―UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),‖ in 2015 Military Communications and Information Systems Conference (MilCIS), Nov. 2015, pp. 1–6. doi: 10.1109/MilCIS.2015.7348942.
R. R. Reddy, Y. Ramadevi, and K. V. N. Sunitha, ―Effective discriminant function for intrusion detection using SVM,‖ in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Sep. 2016, pp. 1148–1153. doi: 10.1109/ICACCI.2016.7732199.
S. KishorWagh, V. K. Pachghare, and S. R. Kolhe, ―Survey on Intrusion Detection System using Machine Learning Techniques,‖ Int. J. Comput. Appl., vol. 78, no. 16, pp. 30–37, Sep. 2013, doi: 10.5120/13608-1412.
S. Othman, F. Ba‑Alwi, T. Nabeel, and A. Al‑Hashida, ―Intrusion detection model using machine learning algorithm on Big Data environment,‖ J. Big Data, vol. 5, Sep. 2018, doi: 10.1186/s40537-018-0145-4.
S. Layeghy, M. Baktashmotlagh, and M. Portmann, ―DI-NIDS: Domain Invariant Network Intrusion Detection System.‖ arXiv, Oct. 15, 2022. Accessed: Oct. 21, 2022. [Online]. Available: http://arxiv.org/abs/ 2210.08252.
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