Document Type : Original Research Paper

Authors

• Seyed Hamid Zahiri
• Mahdi Moodi

Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.

Abstract

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.

Keywords

• Squid Game Optimization
• Data Fusion
• Android Botnet Detection
• SVM
• DT

Main Subjects

• Optimization

Open Access

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Publisher

Shahid Rajaee Teacher Training University