Intrusion Detection System with Ensemble Machine Learning Approaches using VotingClassifier


Authors : Karuna G. Bagde; Atul D. Raut

Volume/Issue : Volume 9 - 2024, Issue 6 - June


Google Scholar : https://tinyurl.com/25ctcy6m

Scribd : https://tinyurl.com/ye27ubu9

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN659

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : Internets have become a part of our everyday life due to the advancement in the electronics and signal processing technologies during past decades. The tremendous growth of internet leads towards the network threats. Many times firewalls and anti-viruses fails to manage the network because of this Intrusion Detection System (IDS) comes to assists us. In this paper we use IDS with Ensemble methodologies utilized in machine learning involve the fusion of multiple classifiers to improve predictive performance, while voting classifiers combine predictions from individual models to reach conclusive decisions. The paper employs a voting ensemble method combing decision tree, logistic regression and support vector machine classifier models. We test our proposedmodel to classify the NSL-KDD dataset. Our ensemble methodologies of proposed algorithmproduce a good result.

Keywords : Intrusion Detection System, Ensemble Algorithm, Machine Learning.

References :

  1. Hanaa, Attou., Azidine, Guezzaz., Said, Benkirane., Mourade, Azrour., Yousef, Farhaoui (2023), “Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques. Big data mining and analytics”, doi: 10.26599/bdma.2022.9020038
  2. Ramesh, Boraiah. (2023), “Network intrusion detection and classification using machine learning predictions fusion”, Indonesian Journal of Electrical Engineering and Computer Science, doi: 10.11591/ijeecs.v31.i2.pp1147-1153
  3. Mutyalaiah, Paricherla., Mahyudin, Ritonga., Sandip, R., Shinde., Smita, M., Chaudhari., Rahmat, Linur., Abhishek, Raghuvanshi. (2023), “Machine learning techniques for accurate classification and detection of intrusions in computer network”, Bulletin of Electrical Engineering          and          Informatics,           doi: 10.11591/beei.v12i4.4708
  4. “Machine learning techniques for accurate classification and detection of intrusions in computer network”, Bulletin of Electrical Engineering and Informatics,               doi: 10.11591/eei.v12i4.4708
  5. Pierpaolo, Dini., Abdussalam, Elhanashi., Andrea, Begni., Sergio, Saponara., Qinghe, Zheng., Kaouther, Gasmi. (2023), “Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity”, Applied Sciences, doi: 10.3390/app13137507
  6. Ch. Sai Sampath, Dr. P. Anuradha (2023), “Intrusion Detection using Machine Learning: A Random Forest-based Approach”, International Journal For Multidisciplinary Research, doi: 10.36948/ijfmr.2023.v05i03.3408
  7. D. Xuan, H. Hu, B. Wang and B. Liu , “Intrusion Detection System Based on RF-SVM Model Optimized with Feature Selection”, 2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), Beijing, China, 2021, pp. 1-5, doi: 10.1109/CCCI52664.2021.9583206.
  8. Sarker, I.H.; Abushark, Y.B.; Alsolami, F.; Khan, A.I., “IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection   Model”, Symmetry2020,12,754 https://doi.org/10.3390/sym12050754
  9. E. Vishnu Balan, M.K. Priyan, C. Gokulnath, G. Usha Devi, “Fuzzy Based Intrusion Detection Systems in MANET” Procedia Computer Science, Volume 50,2015,Pages 109-114,ISSN 1877-0509, https://doi.org/10.1016/j.procs.2015.04.071.

Internets have become a part of our everyday life due to the advancement in the electronics and signal processing technologies during past decades. The tremendous growth of internet leads towards the network threats. Many times firewalls and anti-viruses fails to manage the network because of this Intrusion Detection System (IDS) comes to assists us. In this paper we use IDS with Ensemble methodologies utilized in machine learning involve the fusion of multiple classifiers to improve predictive performance, while voting classifiers combine predictions from individual models to reach conclusive decisions. The paper employs a voting ensemble method combing decision tree, logistic regression and support vector machine classifier models. We test our proposedmodel to classify the NSL-KDD dataset. Our ensemble methodologies of proposed algorithmproduce a good result.

Keywords : Intrusion Detection System, Ensemble Algorithm, Machine Learning.

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