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Grant Award View - GA64710-V3
Developing Adversary-Aware Classifiers for Android Malware Detection
GA ID:
GA64710-V3
Agency:
Australian Research Council
Approval Date:
4-Dec-2019
Variation Publish Date:
20-Oct-2021
Variation Date:
12-Oct-2021
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
1-Jan-2020 to 31-Dec-2023
Value (AUD):
$420,000.00
(GST inclusive where applicable)
Varies:
GA64710
- Developing Adversary-Aware Classifiers for Android Malware Detection
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 19/20 Discovery
Grant Program:
Discovery Projects
Grant Activity:
Developing Adversary-Aware Classifiers for Android Malware Detection
Purpose:
Smartphones have become increasingly ubiquitous in people’s everyday life. However, it was reported that one in every five Android applications were actually malware, considering that Android has taken 88% market share of mobile phones. As an effective technique, machine learning has been widely adopted to detect Android malware. However, recent work suggests that deliberately-crafted malware makes machine learning ineffective. In this project, we propose to develop a series of new techniques, such as 1) Android contextual analysis, 2) wrapper-based hill climbing algorithm, and 3) ensemble learning, to solve this problem. The outcomes will help Australia gain cutting edge technologies in adversarial machine learning and mobile security.
GO ID:
GO Title:
Discovery Projects for funding commencing in 2020
Internal Reference ID:
DP20 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
Swinburne University of Technology
Recipient ABN:
13 628 586 699
Grant Recipient Location
Suburb:
HAWTHORN
Town/City:
HAWTHORN
Postcode:
3122
State/Territory:
VIC
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
VIC
Postcode:
3122
Country:
AUSTRALIA