User Feedback Analysis for Mobile Malware Detection

Tal Hadad, Bronislav Sidik, Nir Ofek, Rami Puzis, Lior Rokach

2017

Abstract

With the increasing number of smartphone users, mobile malware has become a serious threat. Similar to the best practice on personal computers, the users are encouraged to install anti-virus and intrusion detection software on their mobile devices. Nevertheless, their devises are far from being fully protected. Major mobile application distributors, designated stores and marketplaces, inspect the uploaded application with state of the art malware detection tools and remove applications that turned to be malicious. Unfortunately, many malicious applications have a large window of opportunity until they are removed from the marketplace. Meanwhile users install the applications, use them, and leave comments in the respective marketplaces. Occasionally such comments trigger the interest of malware laboratories in inspecting a particular application and thus, speedup its removal from the marketplaces. In this paper, we present a new approach for mining user comments in mobile application marketplaces with a purpose of detecting malicious apps. Two computationally efficient features are suggested and evaluated using data collected from the "Amazon Appstore". Using these two features, we show that feedback generated by the crowd is effective for detecting malicious applications without the need for downloading them.

Paper Citation

in Harvard Style

Hadad T., Sidik B., Ofek N., Puzis R. and Rokach L. (2017). User Feedback Analysis for Mobile Malware Detection . In Proceedings of the 3rd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-209-7, pages 83-94. DOI: 10.5220/0006131200830094

in Bibtex Style

@conference{icissp17,
author={Tal Hadad and Bronislav Sidik and Nir Ofek and Rami Puzis and Lior Rokach},
title={User Feedback Analysis for Mobile Malware Detection},
booktitle={Proceedings of the 3rd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2017},
pages={83-94},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006131200830094},
isbn={978-989-758-209-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - User Feedback Analysis for Mobile Malware Detection
SN - 978-989-758-209-7
AU - Hadad T.
AU - Sidik B.
AU - Ofek N.
AU - Puzis R.
AU - Rokach L.
PY - 2017
SP - 83
EP - 94
DO - 10.5220/0006131200830094