Paper Info
Title
Enhancing State-of-the-art Classifiers with API Semantics to Detect Evolved Android Malware
Abstract
Machine learning (ML) classifiers have been widely deployed to detect Android malware, but at the same time the application of ML classifiers also faces an emerging problem. The performance of such classifiers degrades---or called ages---significantly over time given the malware evolution. Prior works have proposed to use retraining or active learning to reverse and improve aged models. However, the underlying classifier itself is still blind, unaware of malware evolution. Unsurprisingly, such evolution-insensitive retraining or active learning comes at a price, i.e., the labeling of tens of thousands of malware samples and the cost of significant human efforts. In this paper, we propose the first framework, called APIGraph, to enhance state-of-the-art malware classifiers with the similarity information among evolved Android malware in terms of semantically-equivalent or similar API usages, thus naturally slowing down classifier aging. Our evaluation shows that because of the slow-down of classifier aging, APIGraph saves significant amounts of human efforts required by active learning in labeling new malware samples.
Year
DOI
Venue
2020
10.1145/3372297.3417291
CCS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security Virtual Event USA November, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7089-9
5
PageRank 
References 
Authors
0.43
17
8
Name
Order
Citations
PageRank
xiaohan zhang1224.77
Yuan Zhang234217.92
Ming Zhong350.43
Daizong Ding4184.04
Yinzhi Cao529718.73
Yukun Zhang6116.11
Mi Zhang743526.25
Min Yang839025.15