Paper Info
Title
The Concept Of Attack Scenarios And Its Applications In Android Malware Detection
Abstract
In this paper, we propose the concept of attack scenarios, which can be learned and selected from a set of malicious applications and described by sets of Android APIs, to characterize Android malware. Because of its characteristics that produce almost no false-positive, attack scenarios can be used as a pre-filter for machine-learning based detectors to enhance the detection performance at low false-positive rate. By combining different machine learning techniques, we demonstrate that the proposed approach can increase the detection rates. To evaluate our approach, we analyze 20,914 Android application containing 3,145 malicious samples on two different machine learning techniques, KNN and SVM. The experiment results show that the proposed approach can raise the detection rate up to 95.9% malware at 1% false positive rate and 95.9% malware at 0.1% false positive rate respectively.
Year
DOI
Venue
2016
10.1109/HPCC-SmartCity-DSS.2016.198
PROCEEDINGS OF 2016 IEEE 18TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS; IEEE 14TH INTERNATIONAL CONFERENCE ON SMART CITY; IEEE 2ND INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS)
Keywords
Field
DocType
Android, malware detection, static analysis, attack scenario, machine learning
False positive rate,Android (operating system),Computer science,Computer security,Support vector machine,Android malware,Real-time computing,Feature extraction,Artificial intelligence,Malware,Statistical classification,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Yu-Chen Chang1776.30
Sheng-De Wang272068.13