Using capa Rules for Android Malware Detection

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Mobile devices have become the go-to for daily tasks like online banking, healthcare management, and personal photo storage, making them prime targets for malicious actors seeking to exploit valuable information. Bad actors often turn to publishing and distributing malware via apps as a lucrative channel for generating illegal and/or unethical profits. 

Android takes a multi-layered approach to combating malware to help keep users safe (more later in the post), but while we continuously strengthen our defenses against malware, threat actors are persistently updating their malware to evade detection. Malware developers used to complete their entire malicious aggression using the common Android app development toolkits in Java, which is easier to detect by reversing the Java bytecode. In recent years, malware developers are increasing the use of native code to obfuscate some of the critical malware behaviors and putting their hopes on obscuration in compiled and symbol-stripped Executable and Linkable Format (ELF) files, which can be more difficult and time-consuming to reveal their true intentions.

To combat these new challenges, Android Security and Privacy Team is partnering with Mandiant FLARE to extend the open-source binary analysis tool capa to analyze native ARM ELF files targeting Android. Together, we improved existing and developed new capa rules to detect capabilities observed in Android malware, used the capa rule matches to highlight the highly suspicious code in native files, and prompted Gemini with the highlighted code behaviors for summarization to enhance our review processes for faster decisions.

In this blog post, we will describe how we leverage capa behavio

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