A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices

Author:

Feng RuitaoORCID,Chen SenORCID,Xie Xiaofei,Meng GuozhuORCID,Lin Shang-WeiORCID,Liu YangORCID

Funder

Singapore Ministry of Education Academic Research Fund Tier 1

National Research Foundation, Prime Ministers Office, Singapore through its National Cybersecurity Research and Development Program

Singapore National Research Foundation through NCR

NRF Investigatorship

National Research Foundation, Prime Ministers Office, Singapore through NCR

National Natural Science Foundation of China

NVIDIA AI Tech Center

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Cited by 76 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Detecting android malware using deep learning algorithms: A survey;Computers and Electrical Engineering;2024-10

2. Revisiting Temporal Inconsistency and Feature Extraction for Android Malware Detection;2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2024-08-06

3. metaNet: Interpretable unknown mobile malware identification with a novel meta-features mining algorithm;Computer Networks;2024-08

4. Unmasking the Lurking: Malicious Behavior Detection for IoT Malware with Multi-label Classification;Proceedings of the 25th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems;2024-06-20

5. Efficient malware detection through inter-component communication analysis;Cluster Computing;2024-06-02

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