Multilevel and multisource data fusion approach for network intrusion detection system using machine learning techniques

Harshitha Somashekar, Pramod Halebidu Basavaraju

Abstract


To enhance the performance of network intrusion detection systems (NIDS), this paper proposes a novel multilevel and multisource data fusion approach, applied to NSL-KDD and UNSW-NB15 datasets. The proposed approach includes three various levels of operations, which are feature level fusion, dimensionality reduction, and prediction level fusion. In the first stage features of NSL-KDD and UNSW-NB15 both datasets are fused by applying the inner join joint operation by selecting common features like protocol, service and label. Once the data sets are fused in the first level, linear discriminant analysis is applied for 12 feature columns which is reduced to a single feature column leading to dimensionality reduction at the second level. Finally, in the third level, the prediction level fusion technique is applied to two neural network models, where one neural network model has a single input node, two hidden nodes, and two output nodes, and another model having a single input node, three hidden nodes, and two output nodes. The outputs obtained from these two models are then fused using a prediction fusion technique. The proposed approach achieves a classification accuracy of 97.5%.

Keywords


Data fusion; Dimensionality reduction; Network intrusion detection; Neural network; Prediction level fusion

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DOI: http://doi.org/10.11591/ijece.v15i4.pp3938-3948

Copyright (c) 2025 Harshitha Somashekar, Pramod Halebidu Basavaraju

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by theĀ Institute of Advanced Engineering and Science (IAES).