Malware Detection Capabilities for Mobile Devices Running Android Os by Monitoring Hardware Resource Consumption
Year: 2025
Type: article
Abstract: The aim of the article is to explore the potential for detecting malicious software (malware) on devices running the Android operating system by analyzing hardware resource usage statistics. Modern malicious applications exhibit diverse resource consumption patterns, enabling the detection of their anomalous behavior. This study examines how malware affects device performance, operates in the background, and the indicators that can reveal its presence. Unlike traditional signature-based methods, monitoring parameters such as CPU usage, RAM utilization, network activity, and power consumption allows for the detection of sophisticated malicious mobile applications. Examples of malware, such as trojan miners, adware trojans, and spyware, are provided, along with an analysis of detection methods based on monitoring key device performance indicators. Approaches to their detection, including dynamic analysis in sandbox environments, the use of built-in OS utilities, and runtime environment emulation to uncover hidden threats, are discussed. The limitations and drawbacks of hardware resource monitoring - such as high system overhead and potential false positives - are also examined. To improve detection effectiveness, modifications to existing methods are proposed, such as introducing additional analysis metrics, dynamically adjusting data collection intervals based on device load, and applying machine learning for anomaly classification. These enhancements could reduce false positives and increase detection accuracy, making hardware resource monitoring a more promising complement to traditional antivirus approaches.
Institution: Moscow State Linguistic University
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413636403