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All Journal International Journal of Electrical and Computer Engineering JOIV : International Journal on Informatics Visualization
Abidin, Zaheera Zainal
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Computer Science & IT Electrical & Electronics Engineering

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Impact of Data Balancing and Feature Selection on Machine Learning-based Network Intrusion Detection Barkah, Azhari Shouni; Selamat, Siti Rahayu; Abidin, Zaheera Zainal; Wahyudi, Rizki
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1041

Abstract

Unbalanced datasets are a common problem in supervised machine learning. It leads to a deeper understanding of the majority of classes in machine learning. Therefore, the machine learning model is more effective at recognizing the majority classes than the minority classes. Naturally, imbalanced data, such as disease data and data networking, has emerged in real life. DDOS is one of the network intrusions found to happen more often than R2L. There is an imbalance in the composition of network attacks in Intrusion Detection System (IDS) public datasets such as NSL-KDD and UNSW-NB15. Besides, researchers propose many techniques to transform it into balanced data by duplicating the minority class and producing synthetic data. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) algorithms duplicate the data and construct synthetic data for the minority classes. Meanwhile, machine learning algorithms can capture the labeled data's pattern by considering the input features. Unfortunately, not all the input features have an equal impact on the output (predicted class or value). Some features are interrelated and misleading. Therefore, the important features should be selected to produce a good model. In this research, we implement the recursive feature elimination (RFE) technique to select important features from the available dataset. According to the experiment, SMOTE provides a better synthetic dataset than ADASYN for the UNSW-B15 dataset with a high level of imbalance. RFE feature selection slightly reduces the model's accuracy but improves the training speed. Then, the Decision Tree classifier consistently achieves a better recognition rate than Random Forest and KNN.
Smartphone data privacy and security awareness among university students in Malaysia Al-Rassas, Ahmed; Abidin, Zaheera Zainal
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp850-862

Abstract

This study examines the level of data privacy and security awareness (DPSA) among Malaysian university students who depend on smartphones for academic activities. An enhanced cybersecurity education (CE) technological proficiency–perceived control (CTP) model is proposed, incorporating technological innovation and cultural norms (TICN) as a mediating factor between technological proficiency (TP) and awareness. A total of 356 students from public and private institutions in Melaka participated. The Krejcie and Morgan table was used to determine the sample size. Descriptive analysis was conducted using IBM SPSS 27, and SmartPLS-SEM was used to evaluate both measurement and structural models. Reliability and validity were confirmed through a pilot study with 50 respondents. Findings show that TICN significantly strengthens the translation of technical skills into protective behavior, outperforming the original model that used frequency of smartphone usage (FSU) as a mediator. The enhanced model provides a deeper understanding of the socio-technical determinants of smartphone privacy awareness. Implications, limitations, and directions for future research are also discussed.
Co-Authors Al-Rassas, Ahmed Azhari Shouni Barkah Rizki Wahyudi Selamat, Siti Rahayu