MLIDS: A Machine Learning-Based Intrusion Detection System Using the NSLKDD Data
Keywords:
Intrusion detection system, Machine Learning, Classification, SVM, NB, LR, DTAbstract
In order to protect computer networks from malicious activity, intrusion detection systems (IDS) are essential. Traditional rule-based IDS frequently experience significant false positive rates and struggle to keep up with changing attack techniques. This paper, utilizing the NSL-KDD dataset, suggests a machine learning strategy for intrusion detection to overcome these issues. The suggested method makes use of the capabilities of machine learning algorithms to efficiently identify and categorise network intrusions. The proposed model is trained on and tested against the NSL-KDD dataset, a benchmark dataset for intrusion detection research. The data was split into four feature subsets that were taken from the NSL-KDD dataset in order to evaluate the performance of these classifiers. Preprocessing techniques were used to remove pointless attributes from the dataset because an IDS's performance is influenced by the dimensions of the data. In this study, multiple machine learning (ML) classifiers were used to categorise data in an intrusion detection system (IDS) as either normal or invasive. The recommended machine learning-based IDS works brilliantly in terms of various accuracy parameters, according to testing results. When compared to traditional rule-based systems, the suggested method has improved detection rates and fewer false positives, improving the overall security of computer networks.
Downloads
References
L. Sun, A. Ho, Z. Xia, J. Chen, M. Zhang, "Development of an Early Warning System for Network Intrusion Detection using Benford's Law Features", in Security and Privacy in Social networks and big data SocialSec. Singapore: Communications in computer and information science, Springer, 2019,vol.1095, pp. 57–73, 2019.
L. Lv, W. Wang, Z. Zhang, X. Liu, " A novel intrusion detection system based on optimal hybrid kernel extreme learning machine", Knowledgebased systems, vol. 195, pp. 1-17, 2020.
M. Darkaie, R. Tavoli, “Providing a method to reduce the false alarm rate in network intrusion detection systems using the multilayer Perceptron technique and backpropagation algorithm”, in 5th Conference on Knowledge-Based Engineering and Innovation, Tehran, Iran, 2019, pp.1-6
W.Wang, Y. Li, X. Wang, J. Liu, X. Zhang, “ Detecting android malicious apps and categorizing benign apps with ensemble of classifiers”, Future Generation Computing System, vol. 78, pp. 987–94, 2018.
H. Alazzam, A. Sharieh, K.E. Sabri, "A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer", Expert systems with applications, vol. 148, pp.1-14, 2020.
M. Safaldin, M. Otair, L. Abualigah, “Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks”, Journal of ambient intelligence and humanized computing, pp. 1-18, 2020.
K. Chisholm, C. Yakopcic, M.S.Alam, T.M. Taha, “Multilayer perceptron algorithms for network intrusion detection on portable low power hardware”, in 10th annual computing and communication workshop and conference (CCWC), Las Vegas, USA, 2020, pp. 901- 906.
M.A. Ferrag, L. Maglaras, A. Ahmim, M. Derdour, H. Janicke, "RDTIDS: Rules and decision tree-based intrusion detection system for internet of things networks", Future internet,vol. 12, no.3, pp.1-14, 2020.
T.B.Prasad, P.S.Prasad, K.P.Kumar, " An intrusion detection system software program using KNN nearest neighbors approach", International journal of science research and innovation engineering (IJSRIE), vol. 1, pp.1-6, 2020.
S.Rajagopal, P.P. Kundapur, K.S.Hareesha, “A stacking ensemble for network intrusion detection using heterogeneous datasets”, Secure Communication Networks, pp.1–9, 2020.
F. Salo, A.B.Nassif, A. Essex, “Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection”, Computer Networks, vol. 148, pp. 164-175, 2019.
H. Sarker, Y.B. Abushark, F. Alsohami, A.I. Khan, “IntruDTree: A machine learning based cyber-security intrusion detection model”, Symmetry, vol.12, no.5, pp. 1-15, 2020.
C. Ambikavathi, S.K.Srivatsa, “Predictor selection and attack classification using random forest for intrusion detection”, Journal of scientific and industrial research, vol. 79, pp. 365-68, 2020.
Bachar, N. E. Makhfi, O.E. Bannay, "Towards a behavioral network intrusion detection system based on the SVM model", in 2020 1st international conference on innovation research in applied science, engineering and technology (IRASET), Meknes, Morocco, 2020, pp. 1- 7.
D. Wang, G.Xu, “Research on the Detection of Network Intrusion Prevention with SVM Based Optimization Algorithm”, Informatica, vol. 44, pp. 269-273, 2020.
S. Thaseen, B. Poorva, P. S. Ushasree, “ Network Intrusion Detection using Machine Learning Techniques”, in 2020 International Conference on Emerging Trends in Information Technology and Engineering (icETITE), Vellore, India, 2020, pp.1-7.
H. Alazzam, A. Sharieh, K. Sabri, “ A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer", Expert Systems with Applications, vol. 148, pp. 1-14, 2020.
N. Kunhare, R. Tiwari and J. Dhar, "Particle swarm optimization and feature selection for intrusion detection system", Sadhana, vol. 45, no. 109, pp.1-14, 2020.
M.Sarnovsky, J. Paralic, “Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model”, Symmetry, vol. 12, no. 203, pp.1-14, 2020.
R. A. Ghazy, E. S. M. E. Rabaie, M. I. Dessouky, N. A. E. Fishawy · Fathi E. Abd E. Samie, Feature Selection Ranking and SubsetǦ Based Techniques with Different Classifiers for Intrusion Detection, “Wireless Personal Communications”, vol. 111, pp. 375–393, 2020.
S. Waskle, L. Parashar, “Intrusion Detection System Using PCA with Random Forest Approach”, in Proceedings of the International Conference on Electronics and Sustainable Communication Systems (ICESC 2020), Coimbatore, India, 2020, pp. 803-808.
M. A. Ferrag, L. Maglaras, A. Ahmim, M. Derdour, H. Janicke, “RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks”, Future Internet, vol. 12, no. 44, pp. 1- 14, 2020.
P. Nancy, S. Muthurajkumar, S. Ganapathy, S.V.N. Santhosh Kumar, M. Selvi, K. Arputharaj, “ Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks”, IET Commununications, vol. 14, no. 5, pp. 888-895, 2020.
Ahmad, M. Basheri, M.J.Iqbal, A.Rahim, “Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection”, IEEE Access, vol.6, pp. 33789– 33795, 2018.
W. Li, P. Yi, Y. Wu, L. Pan, J. Li, "A new intrusion detection system based on KNN classification algorithm in wireless sensor network", Journal of Electrical and Computer Engineering, pp. 1–8, 2014.
Y. Yao, W. Yang, F. Gao, Ge. Yu, "Anomaly intrusion detection using hybrid MLP/CNN neural network", in Proceedings of the sixth international conference on intelligent system design and applications (ISDA'06), Jinan, 2006, pp.1095-1102.
S.Peddabachigari, A. Abraham, C. Grosan, J. Thomas, “Modeling intrusion detection system using hybrid intelligent systems”, Journal of Network and Comput Applications, vol. 30, pp. 114–132, 2007.
Q. Zhoua, H. Zhoua, T. Lib, “Cost -sensitive feature selection using random forest: Selecting low-cost subsets of informative features”, Knowledge-based systems, vol. 95, pp. 1-11, 2016.
Andrew Hernandez, Stephen Wright, Yosef Ben-David, Rodrigo Costa, David Botha. Intelligent Decision Making: Applications of Machine Learning in Decision Science. Kuwait Journal of Machine Learning, 2(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/197
Reddy V, S. ., Madhav, V. ., M, B. ., Krishna A, A. ., A, K. ., Inthiyaz, S. ., & Ahammad, S. H. . (2023). Hybrid Autonomous Vehicle (Aerial and Grounded). International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 103–109. https://doi.org/10.17762/ijritcc.v11i1.6056
Dhabliya, D., Sharma, R. Cloud computing based mobile devices for distributed computing (2019) International Journal of Control and Automation, 12 (6 Special Issue), pp. 1-4.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
