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All Journal International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Mohd Satar, Siti Dhalila
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Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Addition chain heuristics in application to elliptic curve cryptosystems Mohamed, Mohamad Afendee; Shawai, Yahaya Garba; Derahman, Mohd Noor; Mamat, Abd Rasid; Mohd Satar, Siti Dhalila; Amri Abidin, Ahmad Faisal; Abdul Kadir, Mohd Fadzil
International Journal of Advances in Applied Sciences 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/ijaas.v13.i3.pp546-555

Abstract

The idea of an addition chain can be applied to scalar multiplication involving huge number operations in elliptic curve cryptosystems. In this article, initially, we study the taxonomy of the addition chain problem to build up an understanding of the problem. We then examine the mathematics behind an optimal addition chain that includes the theoretical boundary for the upper limit and lower limit which laid the foundation for experimentation hereafter. In the following, we examine different addition chain solutions that were used to increase efficiency in scalar multiplication. To avoid any possible confusion, we intentionally separated the discussion into two modules called integer recoding method and chain generator based on the heuristics method. These methods were developed by considering various aspects such as the space within which the operation is executed, the curve that is selected, the formulation to express the original equation, and the choices of operation and arithmetic, all together to improve operational efficiency.
Botnet detection: a system for identifying DGA-based botnets using LightGBM Mohamad, Mumtazimah; Abd Hamid, Nazirah; A. Ghaleb, Sanaa A.; Mohd Satar, Siti Dhalila; Safei, Suhailan; Fazamin Wan Hamzah, Wan Mohd Amir; En En, Lim
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 A. Ghaleb, Sanaa A. Abd Hamid, Nazirah Abdul Kadir, Mohd Fadzil Amri Abidin, Ahmad Faisal Derahman, Mohd Noor En En, Lim Fazamin Wan Hamzah, Wan Mohd Amir Mamat, Abd Rasid Mohamad, Mumtazimah Mohamed, Mohamad Afendee Safei, Suhailan Shawai, Yahaya Garba