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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Nazirah Abd Hamid
Universiti Sultan Zainal Abidin

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A Conceptual Model of Role Based Access Control Using Role Mining Algorithm Nazirah Abd Hamid; Rabiah Ahmad; Siti Rahayu Selamat
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1394-1400

Abstract

Numerous studies have shown that currently, role-based access control has becoming one of the successful access control model because of its principle that could simplifies the work of security administrators. However, to construct a concise, role-based access control system, a good role mining algorithm structure is needed therefore the objectives of this paper are firstly, to provide a general overview on phases that involved in designing and developing the algorithm and secondly, to introduce a conceptual model that constructed based on the analysis and this model represents a general process in role mining model. This model involved series of phases that begin with the input of data, pre-processing stage, candidate role generation phase, role selection and role assignment process and lastly number of roles as generated output.
Botnet detection: a system for identifying DGA-based botnets using LightGBM Mumtazimah Mohamad; Nazirah Abd Hamid; Sanaa A. A. Ghaleb; Siti Dhalila Mohd Satar; Suhailan Safei; Wan Mohd Amir Fazamin Wan Hamzah; Lim En En
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp833-844

Abstract

Botnets present a major challenge to detecting anomalies in domain generation algorithms (DGAs). Botmasters use DGAs to create numerous domain names to communicate with command-and-control servers, complicating the detection process. Traditional blacklisting methods struggle to effectively identify anomalous DGA domain names amid the vast number of randomly generated domains, leading to a greater risk of detection being evaded. The proliferation of DGA-based botnets has created an urgent need for robust detection methods. Various techniques and attributes have been utilised to categorise different DGA families, yet the dynamic nature of DGA domain names renders the current blacklisting algorithms ineffective. Additionally, the dynamic characteristics of DGAs further complicate classification, emphasising the need for machine learning models to improve detection accuracy and enhance cyber defence. This study proposes a robust solution to address the challenges posed by DGA-based botnets by developing an innovative machine learning-based model for domain name classification. The model leverages the light gradient boosting algorithm (LightGBM) and integrates n-gram features to enhance the detection of malicious DGA domains. This approach offers superior accuracy, adaptability, and efficiency in identifying and classifying anomalous domain names, achieving 96% precision when detecting true DGA domains. This system represents a significant advancement in cybersecurity and anomaly detection.
Co-Authors Lim En En Mumtazimah Mohamad Rabiah Ahmad Sanaa A. A. Ghaleb Siti Dhalila Mohd Satar Siti Rahayu Selamat Suhailan Safei Wan Mohd Amir Fazamin Wan Hamzah