Fast & Furious: On the modelling of malware detection as an evolving data stream-Reference-Cited by-同舟云学术

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Fast & Furious: On the modelling of malware detection as an evolving data stream

Published:2023-02 Issue: Volume:212 Page:118590
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Ceschin Fabrício^ORCID,Botacin Marcus^ORCID,Gomes Heitor Murilo,Pinagé Felipe,Oliveira Luiz S.,Grégio André

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference50 articles.

1. AndroZoo: Collecting millions of android apps for the research community;Allix,2016

2. Learning to evade static PE machine learning malware models via reinforcement learning;Anderson,2018

3. Android 7.0 behavior changes;Android,2016

4. Accountmanager;Android,2018

5. Device admin deprecation;Android,2018

Cited by 14 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. Strengthening LLM ecosystem security: Preventing mobile malware from manipulating LLM-based applications;Information Sciences;2024-10

2. MeMalDet: A memory analysis-based malware detection framework using deep autoencoders and stacked ensemble under temporal evaluations;Computers & Security;2024-07

3. Online semi-supervised active learning ensemble classification for evolving imbalanced data streams;Applied Soft Computing;2024-04

4. Machine Learning for Android Malware Detection: Mission Accomplished? A Comprehensive Review of Open Challenges and Future Perspectives;Computers & Security;2024-03

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

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