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Mohamad, Mumtazimah
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Improved moth search algorithm with mutation operator for numerical optimization problems Ghaleb, Sanaa A. A.; Mohamad, Mumtazimah; Mohammed Ghanem, Waheed Ali Hussein; Alhadi, Arifah Che; Nasser, Abdullah B.; Aldowah, Hanan
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1022-1031

Abstract

The moth search algorithm (MSA) is a meta-heuristic optimization technique inspired by moth behavior, has shown remarkable efficacy in solving optimization challenges. However, its poor exploration capability results in an imbalance between exploitation and exploration. To address this issue, this research introduces a new mutation operator to enhance exploration by increasing population diversity. The proposed enhanced moth search algorithm (EMSA) aims to expedite convergence and improve overall robustness by exploring new solutions more effectively. Evaluation on ten benchmark functions demonstrates EMSA's superior exploration capabilities, efficiently tackling optimization problems and yielding more optimal solutions within the search space. Compared to conventional MSA and other established algorithms, EMSA delivers well-balanced results, showcasing its effectiveness in optimizing the search space. In the future, the EMSA could potentially find applications in addressing real-world engineering optimization challenges.
Enhancing IoT security: a hybrid intelligent intrusion detection system integrating machine learning and metaheuristic algorithm Ghaleb, Sanaa A. A.; Mohamad, Mumtazimah; Ghanem, Waheed; Ngah, Amir; Yunus, Farizah; Alhadi, Arifah Che; Islam Siddique, MD Nurul
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1040-1049

Abstract

The rapid proliferation of the internet of things (IoT) has introduced significant security and privacy challenges. As IoT devices often have limited computational power and memory, they are highly vulnerable to cyber threats. Traditional intrusion detection systems (IDS) struggle to operate efficiently in these constrained environments, necessitating more adaptive and optimized security solutions. To address these challenges, this study proposes an innovative IDS model, MSAMLP, which combines the moth search algorithm (MSA) with a multilayer perceptron (MLP) classifier. The objective is to enhance the classification accuracy of malicious and benign network traffic while maintaining computational efficiency. The model was evaluated using two widely recognized intrusion detection datasets, benchmarking its performance against existing IDS approaches. Experimental results indicate that MSAMLP outperforms conventional classification models, achieving high accuracy, improved detection rates, and reduced false alarm rates. Its adaptive learning capability ensures better anomaly detection in dynamic IoT environments. In conclusion, the proposed MSAMLP model demonstrates superior performance in securing IoT networks, offering an effective solution to mitigate evolving cyber threats. This research contributes to the advancement of IoT security by introducing a robust and scalable intrusion detection approach.
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 Aldowah, Hanan Alhadi, Arifah Che En En, Lim Fazamin Wan Hamzah, Wan Mohd Amir Ghaleb, Sanaa A. A. Ghanem, Waheed Islam Siddique, MD Nurul Mohammed Ghanem, Waheed Ali Hussein Mohd Satar, Siti Dhalila Nasser, Abdullah B. Ngah, Amir Safei, Suhailan Yunus, Farizah