Zero-day Android botnet detection using neural networks

Seraj, Saeed; Pimenidis, Elias; Trovati, Marcello; Polatidis, Nikolaos

Abstract

Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security.

• Journal Article Type: Article
• Acceptance Date: Nov 20, 2024
• Online Publication Date: Dec 16, 2024
• Publication Date: Jun 30, 2025
• Deposit Date: Nov 25, 2024
• Publicly Available Date: Jun 18, 2025
• Journal: Neural Computing and Applications
• Print ISSN: 0941-0643
• Electronic ISSN: 1433-3058
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• Volume: 37
• Issue: 17
• Pages: 10795-10805
• DOI: https://doi.org/10.1007/s00521-024-10818-7
• Keywords: Android malware detection, Neural networks, New dataset, Botnets
• Public URL: https://uwe-repository.worktribe.com/output/13463890