Ensemble Based Intrusion Detection System for Multi attack Environment

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Satyapriya S. Raut; Aniketh R. Poojary; Aditya C. Naiknaware; Sushant G. Vairat; Prof. Shraddha R. Khonde

Ensemble Based Intrusion Detection System for Multi attack Environment

Authors: Satyapriya S. Raut, Aniketh R. Poojary, Aditya C. Naiknaware, Sushant G. Vairat, Prof. Shraddha R. Khonde

Unique Paper ID: 149288
Volume: 6
Issue: 11
PageNo: 687-690

Keywords: Ensemble Intrusion Detection System Machine Learning XGBoost Random Forest Extra Tree Bagging Boosting Stacking NSL-KDD.

Abstract:
Due to mass usage of Internet in todayâ€™s era, cyber-attacks have been very common. These pose a serious threat to the organization as well as for an individual. The sensitive and confidential data that needs to be protected is at high risk and is stolen by attackers using various types of attacks. In multi attack environment, there would be more than one attack occurring simultaneously or within a short span of time. In our project, we have considered all those attacks as multi attacks which occur within one second of time span. We have proposed a system that captures live packets from the network and classifies whether the packet is normal or belongs to one of the subclasses of attack using various ensemble approaches such as Bagging, Boosting and Stacking. NSL-KDD dataset has been used for both training and testing the model. We found out that XGBoost outperforms with highest accuracy, 72.27%, followed by Random Forest classifier, 72.22%.

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Cite This Article

ISSN: 2349-6002
Volume: 6
Issue: 11
PageNo: 687-690

Ensemble Based Intrusion Detection System for Multi attack Environment

Available:https://ijirt.org/article?manuscript=149288

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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