@inproceedings{254a2fe6ffae4981be7b481d125b608f,
title = "Andro-Simnet: Android Malware Family Classification using Social Network Analysis",
abstract = "While the rapid adaptation of mobile devices changes our daily life more conveniently, the threat derived from malware is also increased. There are lots of research to detect malware to protect mobile devices, but most of them adopt only signature-based malware detection method that can be easily bypassed by polymorphic and metamorphic malware. To detect malware and its variants, it is essential to adopt behavior-based detection for efficient malware classification. This paper presents a system that classifies malware by using common behavioral characteristics along with malware families. We measure the similarity between malware families with carefully chosen features commonly appeared in the same family. With the proposed similarity measure, we can classify malware by malware's attack behavior pattern and tactical characteristics. Also, we apply community detection algorithm to increase the modularity within each malware family network aggregation. To maintain high classification accuracy, we propose a process to derive the optimal weights of the selected features in the proposed similarity measure. During this process, we find out which features are significant for representing the similarity between malware samples. Finally, we provide an intuitive graph visualization of malware samples which is helpful to understand the distribution and likeness of the malware networks. In the experiment, the proposed system achieved 97% accuracy for malware classification and 95% accuracy for prediction by K-fold cross-validation using the real malware dataset.",
keywords = "machine learning, malware classification, malware similarity, social network analysis",
author = "Kim, {Hye Min} and Song, {Hyun Min} and Seo, {Jae Woo} and Kim, {Huy Kang}",
note = "Funding Information: ACKNOWLEDGMENT This work was supported by Samsung Electronics Software R&D Center. Publisher Copyright: {\textcopyright} 2018 IEEE.; 16th Annual Conference on Privacy, Security and Trust, PST 2018 ; Conference date: 28-08-2018 Through 30-08-2018",
year = "2018",
month = oct,
day = "29",
doi = "10.1109/PST.2018.8514216",
language = "English",
series = "2018 16th Annual Conference on Privacy, Security and Trust, PST 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Deng, {Robert H.} and Stephen Marsh and Jason Nurse and Rongxing Lu and Sakir Sezer and Paul Miller and Liqun Chen and Kieran McLaughlin and Ali Ghorbani",
booktitle = "2018 16th Annual Conference on Privacy, Security and Trust, PST 2018",
}