Sampling Integrated Boosting Classifier for Network Intrusion Detection

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Sampling Integrated Boosting Classifier for Network Intrusion Detection

Author: A.Sagayapriya and S.Britto Ramesh Kumar

Published On: 2022-12-30

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Abstract

Increase in networking and communication technologies has resulted in improved lifestyle of people. However, the large and sensitive information being transmitted online has become a target for fraudsters. This has resulted in the need for an effective intrusion detection system to safeguard the critical information. This work presents a two phased intrusion detection model, SIBC, that aims to automatically handle data imbalance and also provide effective intrusion detection. Experiments were performed with KDD cup data, NSL-KDD data, and UNSW-NB15 data. Comparisons indicate that the SIBC model performs effective detection of intrusions with accuracy levels with accuracy levels greater than 90% indicating highly effective predictions.

Keywords: Network Intrusion Detection; Ensemble Modeling; Boosting; Sampling; Data Imbalance

Keywords

Network Intrusion Detection; Ensemble Modeling; Boosting; Sampling; Data Imbalance

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