Bio-inspired Hybrid Intelligent Method for Detecting Android Malware

Today’s smartphones are capable of doing much more than the previous generation of mobile phones. However this extended range of capabilities is coming together with some new security risks. Also, mobile platforms often contain small, insecure and less well controlled applications from various single developers. Due to the open usage model of the Android market, malicious applications cannot be avoided completely. Especially pirated applications or multimedia content in popular demand, targeting user groups with typically low awareness levels are predestined to spread too many devices before being identified as malware. Generally malware applications utilizing root exploits to escalate their privileges can inject code and place binaries outside applications storage locations. This paper proposes a novel approach, which uses minimum computational power and resources, to indentify Android malware or malicious applications. It is a bio-inspired Hybrid Intelligent Method for Detecting Android Malware (HIMDAM). This approach performs classification by employing Extreme Learning Machines (ELM) in order to properly label malware applications. At the same time, Evolving Spiking Neural Networks (eSNNs) are used to increase the accuracy and generalization of the entire model.

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