Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Halebidu Basavaraju, Pramod
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Feature level fusion of multi-source data for network intrusion detection Somashekar, Harshitha; Halebidu Basavaraju, Pramod
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2956-2962

Abstract

The generation of data, collecting, and refining in computer networks have increased exponentially in recent years. Network attacks have also grown in prevalence with this proliferation of data and are now an inherent issue in complicated networks. Current network intrusion detection systems (NIDS) have significant issues with regard to anomaly detection. Several machine learning classification approaches are used to create NIDSs, but they are not sufficiently sophisticated to reliably detect complicated or synthetic attacks, especially if working with a lot of multi-scale data. Data fusion has been used in network intrusion detection to address these issues. For network intrusion detection, we suggested a multi-source data fusion technique in this research, which combines specific features from two datasets to produce a single dataset. Also, a machine learning classifier with fewer parameters is utilized for the fused dataset. The random forest shows the best classification accuracy compared to others in this work. For the normal classification, model accuracy is 92.8%, and the proposed fusion model showed 97.3% accuracies. Furthermore, the findings show that, when compared to other cutting-edge techniques, the suggested model is substantially more effective in detecting intrusions.
Multilevel and multisource data fusion approach for network intrusion detection system using machine learning techniques Somashekar, Harshitha; Halebidu Basavaraju, Pramod
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3938-3948

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%.
Co-Authors Somashekar, Harshitha