Analysis and Detection of Android Stegomalware: the Impact of the Loading Stage

Due to the increasing use of advanced offensive techniques, the mitigation of Android malware is an urgent need. An emerging attack trend exploits steganography to conceal malicious payloads within applications to make attacks stealthier. Even if works on “stegomalware” are starting to emerge, they primarily focus on the multimedia part of the attack chain, i.e., on how to detect hidden data in images or videos. Therefore, this work aims at understanding whether the loading stage required for the extraction of cloaked information can generate detection signatures. To this aim, we develop a proof-of-concept implementation, which has been repacked within a real Android application and tested against several malware detection engines provided by VirusTotal. To anticipate possible offensive campaigns, we also performed tests by considering threat actors able to obfuscate the bytecode of the loader or the entire APK. Results indicate that standard tools are not ready to face stegomalware targeting Android applications. Therefore, we provide indications on how to improve forensics and attribution phases for Android malware endowed with information hiding capabilities.

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Published
2025-06-17
DOI
https://doi.org/10.1145/3733102.3733122
Primary Topic
Advanced Malware Detection Techniques
Type
article
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article

Analysis and Detection of Android Stegomalware: the Impact of the Loading Stage

Angelica Liguori, Giacomo Benedetti, Leonardo Regano, Giorgio Giacinto et al.
Advanced Malware Detection Techniques
article

Analysis and Detection of Android Stegomalware: the Impact of the Loading Stage

Angelica Liguori, Giacomo Benedetti, Leonardo Regano, Giorgio Giacinto, Luca Caviglione, Diego Soi, Silvia Lucia Sanna
article en

Abstract

Due to the increasing use of advanced offensive techniques, the mitigation of Android malware is an urgent need. An emerging attack trend exploits steganography to conceal malicious payloads within applications to make attacks stealthier. Even if works on “stegomalware” are starting to emerge, they primarily focus on the multimedia part of the attack chain, i.e., on how to detect hidden data in images or videos. Therefore, this work aims at understanding whether the loading stage required for the extraction of cloaked information can generate detection signatures. To this aim, we develop a proof-of-concept implementation, which has been repacked within a real Android application and tested against several malware detection engines provided by VirusTotal. To anticipate possible offensive campaigns, we also performed tests by considering threat actors able to obfuscate the bytecode of the loader or the entire APK. Results indicate that standard tools are not ready to face stegomalware targeting Android applications. Therefore, we provide indications on how to improve forensics and attribution phases for Android malware endowed with information hiding capabilities.

University of Cagliari (IT), Consorzio Interuniversitario Nazionale per l'Informatica (IT), National Research Council (IT)
Openalex Percentile: Top 84%
Advanced Malware Detection Techniques
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