Multi-label Classification for Android Malware Based on Active Learning

Author:

Qiao Qijing1,Feng Ruitao2ORCID,Chen Sen1ORCID,Zhang Fei1,Li Xiaohong1ORCID

Affiliation:

1. College of Intelligence and Computing, Tianjin University, China

2. R.Feng is with the CSCRC, University of New South Wales, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Cited by 5 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. DOMR: Toward Deep Open-World Malware Recognition;IEEE Transactions on Information Forensics and Security;2024

2. SeqAdver: Automatic Payload Construction and Injection in Sequence-based Android Adversarial Attack;2023 IEEE International Conference on Data Mining Workshops (ICDMW);2023-12-04

3. Automatic Detection and Analysis towards Malicious Behavior in IoT Malware;2023 IEEE International Conference on Data Mining Workshops (ICDMW);2023-12-04

4. ARdetector: android ransomware detection framework;The Journal of Supercomputing;2023-11-08

5. Multi-labeling of Malware Samples Using Behavior Reports and Fuzzy Hashing;Communications in Computer and Information Science;2023

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