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Zahiri, Seyed Hamid, Moodi, Mahdi. (1405). Android Botnet Detection based on a Novel Intelligent Decision Fusion Method. فناوری آموزش, (), -. doi: 10.22061/jecei.2026.113091.934
Seyed Hamid Zahiri; Mahdi Moodi. "Android Botnet Detection based on a Novel Intelligent Decision Fusion Method". فناوری آموزش, , , 1405, -. doi: 10.22061/jecei.2026.113091.934
Zahiri, Seyed Hamid, Moodi, Mahdi. (1405). 'Android Botnet Detection based on a Novel Intelligent Decision Fusion Method', فناوری آموزش, (), pp. -. doi: 10.22061/jecei.2026.113091.934
Zahiri, Seyed Hamid, Moodi, Mahdi. Android Botnet Detection based on a Novel Intelligent Decision Fusion Method. فناوری آموزش, 1405; (): -. doi: 10.22061/jecei.2026.113091.934

Android Botnet Detection based on a Novel Intelligent Decision Fusion Method

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 11 شهریور 1405
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2026.113091.934
نویسندگان
Seyed Hamid Zahiri* ؛ Mahdi Moodi
Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
تاریخ دریافت: 02 خرداد 1405،  تاریخ بازنگری: 09 مرداد 1405،  تاریخ پذیرش: 17 مرداد 1405 
چکیده
Background and Objectives: An Android botnet refers to a collection of infected Android devices under the control of a cybercriminal, known as a botmaster, which are used to carry out harmful actions like DDoS attacks, spam distribution, data theft, or malware propagation. These botnets present significant risks to user security, privacy, and network stability. Detecting them is difficult due to their adaptive nature, as attackers continually refine their methods to avoid detection. Additionally, Android devices have resource limitations—such as battery life, memory, and processing capacity—that further complicate detection efforts.
Methods: To address this challenge, this paper proposes the SGO DF (Squid Game Optimization-based Data Fusion) method for accurately identifying Android botnets. The approach optimizes eleven parameters using the Squid Game Optimization (SGO) algorithm: eight of these parameters fine-tune machine learning models (SVM, DT, and RF) to enhance their accuracy, while the remaining three adjust weighting in the Data Fusion process.
Results: This optimization enables more precise classification of test data, outperforming five other methods in evaluation.
Conclusion: The results demonstrate that SGO DF achieves an average accuracy exceeding 99% across 28 different Android botnet datasets, showcasing its effectiveness in botnet detection.
کلیدواژه‌ها
Squid Game Optimization؛ Data Fusion؛ Android Botnet Detection؛ SVM؛ DT
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