Publication Type
Journal Article
Version
publishedVersion
Publication Date
9-2024
Abstract
With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users' devices and private data. Many methods have been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methods still suffer from unsatisfactory performance due to two challenges. First, they are unable to address the imbalanced data distribution problem, leading to poor performance for malware families with few members. Second, they are unable to address the zero-day malware (zero-day malware refers to malicious applications that exploit unknown vulnerabilities) classification problem. In this article, we introduce an innovative meta-learning approach for multi-family Android malware classification named Meta-MAMC, which uses meta-learning technology to learn meta-knowledge (i.e., the similarities and differences among different malware families) of few-family samples and combines new sampling algorithms to solve the above challenges. Meta-MAMC integrates (i) the meta-knowledge contained within the dataset to guide models in learning to identify unknown malware; and (ii) more accurate and diverse tasks based on novel sampling strategies, as well as directly adapting metalearning to a new few-sample and zero-sample task to classify families. We have evaluated Meta-MAMC on two popular datasets and a corpus of real-world Android applications. The results demonstrate its efficacy in accurately classifying malicious applications belonging to certain malware families, even achieving 100% classification in some families.
Keywords
Android, malware family, meta-learning, classification
Discipline
Information Security | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ACM Transactions on Software Engineering and Methodology
Volume
33
Issue
7
First Page
1
Last Page
27
ISSN
1049-331X
Identifier
10.1145/3664806
Publisher
Association for Computing Machinery (ACM)
Citation
LI, Yao; YUAN, Dawei; ZHANG, Tao; CAI, Haipeng; LO, David; GAO, Cuiyun; LUO, Xiapu; and JIANG, He.
Meta-learning for multi-family Android malware classification. (2024). ACM Transactions on Software Engineering and Methodology. 33, (7), 1-27.
Available at: https://ink.library.smu.edu.sg/sis_research/9429
Copyright Owner and License
Author-CC-BY
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3664806