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Grant Award View - GA64649
Adversarial Learning of Hybrid Representation
GA ID:
GA64649
Agency:
Australian Research Council
Approval Date:
4-Dec-2019
Publish Date:
5-Dec-2019
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
1-Jan-2020 to 31-Dec-2023
Original: 1-Jan-2020 to 31-Dec-2022
Value (AUD):
$390,000.00
(GST inclusive where applicable)
Variations:
- GA64649-V3 - Variation to Grant (29-Mar-2023 )
- GA64649-V2 - Variation to Grant (20-Oct-2021 )
- GA64649-V1 - Variation to Grant (2-Aug-2021 )
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 19/20 Discovery
Grant Program:
Discovery Projects
Grant Activity:
Adversarial Learning of Hybrid Representation
Purpose:
This project aims to design and implement a foundational deep representation learning framework for early detection, classification and defense of emerging malware by capturing their underlying behaviours via structured and unstructured heterogeneous information through hybrid representation learning, behaviour graph mining, and symbolic adversarial learning to discover and defend unknown malware families, thereby significantly boosting the accuracy and robustness of existing classifiers and detectors. The resulting representation learning framework will enhance the national security to protect user privacy, reducing the multi-million-dollar loss caused by fraudulent transactions, and defending against cyber attacks.
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:
University of Technology Sydney
Recipient ABN:
77 257 686 961
Grant Recipient Location
Suburb:
ULTIMO
Town/City:
ULTIMO
Postcode:
2007
State/Territory:
NSW
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
NSW
Postcode:
2007
Country:
AUSTRALIA