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All Journal International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Lukman Adebayo Ogundele
Olabisi Onabanjo University
Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Random early detection-quadratic linear: an enhanced active queue management algorithm Samuel Oluwatosin Hassan; Vivian Ogochukwu Nwaocha; Adewole Usman Rufai; Tola John Odule; Theophilus Aniemeka Enem; Lukman Adebayo Ogundele; Suleiman Abu Usman
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3875

Abstract

This paper identifies the lone linear drop function for computing the dropping probability between certain queue threshold values as a major weakness for the random early detection (RED) algorithm as it leads to large delay and queue instability. To address this concern, we propose an enhanced RED-based algorithm called random early detection-quadratic linear (referred to as “RED-QL”) active queue management (AQM) which leveraged the benefit of a quadratic packet drop function for a light-to moderate traffic load conditions together with a linear packet drop function for a heavy traffic load condition respectively. Results from ns-3 network simulator using different experimental scenarios clearly reveals that the proposed RED-QL algorithm yields a substantial reduction in delay performance and indeed a reduced average queue size than other three representative AQM algorithms. RED-QL is robust, easy to implement and deploy in routers (both in software and hardware) as no more than the packet drop probability profile of the classic RED’s algorithm implementation needs modest alteration.
Optimization-enabled machine learning with feature selection for phishing detection system Lukman Adebayo Ogundele; Julius Temitayo Adepoju; Femi Emmanuel Ayo; Idayat Abike Akano; Abidemi Emmanuel Adeniyi; Halleluyah Oluwatobi Aworinde; Oluwasegun Julius Aroba; Gbohunmi Ajibesin
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1131-1146

Abstract

The rapid evolution of phishing methods has long been a threat to cybersecurity that demands adaptive and intelligent detection techniques. This current work envisions an improved phishing detection system that involves the integration of machine learning (ML) with feature selection for improving the performance of real-time classification. A comprehensive dataset was optimized and preprocessed through Chi-square (CHI) feature selection to reduce dimensionality while preserving the most discriminative features. Random forest (RF) algorithm, selected for its robustness, interpretability, and high computational efficiency for binary classification, was employed and achieved an accuracy of 96.25%, effectively identifying sophisticated phishing attacks. The architecture is designed to operate on large datasets with minimal computational overhead but with high precision and scalability. Experimental results also indicate the dependability of the system with an area under the curve (AUC)-receiver operating characteristic (ROC) score of 1.0, indicating perfect separation of phishing from normal cases. In addition, an adaptive classification model was used to accommodate unknown data sets with a 100% accuracy score. The results above identify the potential of adaptive, feature-optimized ML systems in the ability to handle dynamic phishing attacks and enable proactive cybersecurity defense.
Co-Authors Abidemi Emmanuel Adeniyi Adewole Usman Rufai Femi Emmanuel Ayo Gbohunmi Ajibesin Halleluyah Oluwatobi Aworinde Idayat Abike Akano Julius Temitayo Adepoju Oluwasegun Julius Aroba Samuel Oluwatosin Hassan Suleiman Abu Usman Theophilus Aniemeka Enem Tola John Odule Vivian Ogochukwu Nwaocha