@inproceedings{c5b8c7e6f633456dae01d92f1b919a84,
title = "DroidAPIMiner: Mining API-level features for robust malware detection in android",
abstract = "The increasing popularity of Android apps makes them the target of malware authors. To defend against this severe increase of Android malwares and help users make a better evaluation of apps at install time, several approaches have been proposed. However, most of these solutions suffer from some shortcomings; computationally expensive, not general or not robust enough. In this paper, we aim to mitigate Android malware installation through providing robust and lightweight classifiers. We have conducted a thorough analysis to extract relevant features to malware behavior captured at API level, and evaluated different classifiers using the generated feature set. Our results show that we are able to achieve an accuracy as high as 99% and a false positive rate as low as 2.2% using KNN classifier.",
keywords = "Android, Classification, Malware, Static detection",
author = "Yousra Aafer and Wenliang Du and Heng Yin",
year = "2013",
doi = "10.1007/978-3-319-04283-1_6",
language = "English (US)",
isbn = "9783319042824",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "86--103",
editor = "Tanveer Zia and Albert Zomaya and Vijay Varadharajan and Morley Mao",
booktitle = "Security and Privacy in Communication Networks - 9th International ICST Conference, SecureComm 2013, Revised Selected Papers",
note = "9th International Conference on Security and Privacy in Communication Networks, SecureComm 2013 ; Conference date: 25-09-2013 Through 28-09-2013",
}