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This Author published in this journals
All Journal Journal of Computing Theories and Applications Scientific Journal of Computer Science
Abdulrashid Abdulrauf
Federal Polytechnic Kaltungo
Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : scientific journal of computer science

 1 
A Lightweight Stacking Ensemble Intrusion Detection Framework for Software-Defined Networking Using the InSDN Dataset Ubakaghinwa Paul Chigbu; Abdulrashid Abdulrauf; Ishaq Isa; Badamasi Usman Zuntu; Maryam Abubakar Sharif
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.511

Abstract

Software-Defined Networking (SDN) has become a key enabler of next-generation communication infrastructures because of its centralized control, programmability, and global network visibility. However, the centralized architecture also introduces significant security vulnerabilities, making SDN environments highly susceptible to attacks such as DoS, DDoS, probing, brute-force, and botnet activities. Although deep learning-based intrusion detection systems have achieved high detection accuracy, many existing approaches suffer from high computational complexity, long training time, and limited suitability for real-time deployment. This study addresses this gap by developing a lightweight stacking ensemble intrusion detection framework for SDN using the InSDN dataset. The proposed framework employs XGBoost, LightGBM, CatBoost, Random Forest, and Extra Trees as base learners, with Logistic Regression serving as the meta-learner. Experiments were conducted using 48-feature, 6-feature, and 4-feature configurations derived from previous feature-reduction studies. The results demonstrate consistently high detection performance, achieving accuracies above 99% across all feature subsets, with only marginal degradation under reduced feature dimensions. The framework showed excellent detection capability for major attack categories while maintaining reliable performance for most minority classes. These findings demonstrate that stacking ensemble learning is a practical and computationally efficient alternative to complex deep learning architectures for SDN intrusion detection, with strong potential for scalable and real-time cybersecurity deployment in modern network environments.
A Binary Firefly Optimized Stacking Ensemble Model for Customer Churn Prediction in the Telecommunications Industry Abdulrashid Abdulrauf; Maaruf Mohammed Lawal; Oluwatoyin Omoloba; Omolara Busayo Abodunrin; Minkail Mohammad
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.507

Abstract

Customer churn prediction remains a critical challenge in the telecommunications industry because subscriber attrition directly affects revenue generation, customer retention, and long-term business sustainability. Although numerous studies have employed machine learning and deep learning techniques for churn prediction, many existing approaches rely on standalone models that have limited ability to simultaneously capture complex nonlinear customer behaviour, exploit model diversity, and eliminate redundant features. Building upon our previous study, this research proposes an enhanced heterogeneous stacking ensemble framework that integrates Binary Firefly Algorithm (BFA)-based feature optimization with Deep Neural Network (DNN), Support Vector Machine (SVM), and Random Forest (RF) as base learners, while Logistic Regression serves as the meta-learner for final churn classification. To ensure a fair and controlled comparison, the same Maven Analytics Telecom Customer Churn dataset and preprocessing strategy adopted in the previous study were retained, including data cleaning, feature transformation, stratified data partitioning, and normalization. Model development further incorporated 5-fold cross-validation on the training dataset, while BFA was introduced to identify the most informative pre-churn features. Experimental results demonstrate that the proposed framework achieved strong and balanced predictive performance and demonstrated improvements in several classification metrics compared with the previously developed BFA-based Hybrid TabNet-DNN model. Furthermore, its performance was competitive with the Random Forest baseline, which exhibited comparable classification effectiveness. These findings show that optimized feature selection and heterogeneous ensemble learning improve prediction stability and generalization. The proposed framework provides an effective decision-support tool for proactive customer retention in the telecommunications industry.
Co-Authors Badamasi Usman Zuntu Ishaq Isa Maaruf M. Lawal Maaruf M. Lawal Maaruf Mohammed Lawal Maryam Abubakar Sharif Minkail Mohammad Oluwatoyin Omoloba Omolara Busayo Abodunrin Philip O. Odion Ubakaghinwa Paul Chigbu