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SensDroid: Analysis for Malicious Activity Risk of Android Application

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Abstract

In Android, the inter-communication structure is governed by a late runtime binding message called Intent. Intents are having rich features which can detect the true nature of malware when compared to another known trait such as permissions. In this work, a framework called SensDroid is formulated that evaluates the efficiency of android intents and permissions as a differentiating trait to spot malicious apps through sensitive analysis technique. Efficiency escalation has been achieved by integrating these traits with other well-known malware detection attributes. The proposed work also uses sufficient number of samples collected from official and third-party Android app market. Multiple parameters are evaluated and compared with the existing techniques. Successful categorization of clean and malware app with high identification rate has been achieved. As a background discussion, we also give a comprehensive review of ancient android application analysis techniques, risk identification techniques, and intent analysis techniques for contemporary malicious activity.

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Acknowledgements

The authors would like to thank their colleagues for many useful comments. In particular, they are grateful to Dr. Jim Lemon from Bitwrit Software, Australia for many discussions on the R programming code.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, National Institute of Technology Patna, Bihar, India

    Gulshan Shrivastava & Prabhat Kumar

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  1. Gulshan Shrivastava
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  2. Prabhat Kumar
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Correspondence to Gulshan Shrivastava.

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Shrivastava, G., Kumar, P. SensDroid: Analysis for Malicious Activity Risk of Android Application. Multimed Tools Appl 78, 35713–35731 (2019). https://doi.org/10.1007/s11042-019-07899-1

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  • DOI: https://doi.org/10.1007/s11042-019-07899-1

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