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Security research on smart devices mostly focuses on malware installation and activation, privilege escalation, remote control, financial charges, personal information stealing, and permission use. Less attention has been paid to the deceptive mechanisms, which are critical for the success of malware on smart devices. Generally, malware first gets installed and then continues operating on the device without attracting suspicion from users. To do so, smart device malware uses various techniques to conceal itself, e.g., hiding activity, muting the phone, and deleting call logs. In this work, we developed an approach to semi-automatically reveal unknown malware hiding techniques. First, it extracts SMH behaviors from malware descriptions by using natural language processing techniques. Second, it maps SMH behaviors to SMH-related APIs based on the analysis of API documents. Third, it performs static analysis on the malware apps that contain unknown SMH behaviors to extract the code segments related to the SMH API calls. For those verified SMH code segments, we describe the techniques used for unknown SMH behaviors based on the code segments. Our experiment tested 119 malware apps with hiding behaviors. The F-measure is 85.58%, indicating that our approach is quite effective.
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This content will become publicly available on December 5, 2025
Multimedia-Based Testbot for Detecting Malware-Hiding Behaviors
Abstract—Less attention has been paid to the deceptive mechanisms of malware on smart devices. Smart device malware uses various techniques to conceal itself, e.g., hiding activity, muting the phone, and deleting call logs. In this work, we developed a novel approach to semi-automatically detect malware hiding behaviors. To more effectively and thoroughly detect malware hiding behaviors, our prototype checks multiple mediums, including vision, sound, vibration, phone calls, messages, and system logs. Our experiments show that the approach can detect malware hiding behaviors. The F-measure is 87.7%, indicating that our approach is quite effective.
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- Award ID(s):
- 2154483
- PAR ID:
- 10590834
- Publisher / Repository:
- Publisher IEEE, 28th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter 2024)
- Date Published:
- ISBN:
- 979-8-3315-3930-6
- Format(s):
- Medium: X
- Location:
- Taiwan
- Sponsoring Org:
- National Science Foundation
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