Deep learning at the shallow end: Malware classification for non-domain experts

被引：116
|
作者：
Le, Quan [1 ]
Boydell, Oisin [1 ]
Mac Namee, Brian [1 ]
Scanlon, Mark [2 ]
机构：
[1] Univ Coll Dublin, Ctr Appl Data Analyt Res, Dublin, Ireland
[2] Univ Coll Dublin, Forens & Secur Res Grp, Dublin, Ireland
来源：
DIGITAL INVESTIGATION | 2018年 / 26卷
关键词：
Deep learning; Machine learning; Malware analysis; Reverse engineering;
D O I：
10.1016/j.diin.2018.04.024
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification. (C) 2018 The Author(s). Published by Elsevier Ltd on behalf of DFRWS.
引用
收藏
页码：S118 / S126
页数：9
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