ORCID Profile
0000-0002-5516-9984
Current Organisation
Monash University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Software Engineering | Computer Software |
Expanding Knowledge in the Information and Computing Sciences | Expanding Knowledge in Technology
Publisher: Association for Computing Machinery (ACM)
Date: 23-12-2022
DOI: 10.1145/3544968
Abstract: Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual rules or traditional machine learning may not be effective. In recent years, a dominant research field called deep learning (DL), which provides a powerful feature abstraction ability, has demonstrated a compelling and promising performance in a variety of areas, like natural language processing and computer vision. To this end, employing DL techniques to thwart Android malware attacks has recently garnered considerable research attention. Yet, no systematic literature review focusing on DL approaches for Android malware defenses exists. In this article, we conducted a systematic literature review to search and analyze how DL approaches have been applied in the context of malware defenses in the Android environment. As a result, a total of 132 studies covering the period 2014–2021 were identified. Our investigation reveals that, while the majority of these sources mainly consider DL-based Android malware detection, 53 primary studies (40.1%) design defense approaches based on other scenarios. This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.
Publisher: ACM
Date: 27-05-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 05-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: ACM
Date: 14-05-2016
Publisher: ACM
Date: 14-05-2016
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 16-09-2202
Publisher: Elsevier BV
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: IEEE
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IEEE
Date: 07-2013
DOI: 10.1109/SNPD.2013.92
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 05-2015
DOI: 10.1109/ICSE.2015.93
Publisher: IEEE
Date: 03-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Springer Science and Business Media LLC
Date: 08-09-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Start Date: 03-2020
End Date: 03-2023
Amount: $392,778.00
Funder: Australian Research Council
View Funded Activity