Malware detection on Smartphones potentially enables the quick and timely mitigation of attacks that might originate from these devices. Typical malware detection methods known from fixed networks heavily rely on signature based detection of malware. Signature-based detection enables a very high detection rate, but is ultimately dependent on the a-priori knowledge of the kind of malware it aims to detect. If the malware is known and a signature is available, it will be detected. If the malware is unknown or employs advances hiding techniques such as encryption, polymorphism, obfuscation, or packing, the signature-based detection will not be successful. It is obvious that signature-based malware detection is needed, but has its limitation especially in the context of Smartphones and the diverse application landscape. A new approach to malware detection is based on behavioral aspects of the application and also the behavior of the users. This talk will introduce various detection mechanisms leveraging different behavioral aspects that can be extracted from Smartphones. Additionally we offer some ideas for future directions and application scenarios.

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