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 :
- 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
- 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
- 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
- “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
- 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
- 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
- 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.
- 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
- 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.