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Multi-labeling of complex, multi-behavioral malware samples

  • Published:2022-10 Issue: Volume:121 Page:102845
  • ISSN:0167-4048
  • Container-title:Computers & Security
  • language:en
  • Short-container-title:Computers & Security

Author:

García-Teodoro P.ORCID,Gómez-Hernández J.A.ORCID,Abellán-Galera A.ORCID

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference76 articles.

1. Are we missing labels? a study of the availability of ground-truth in network security research;Abt,2014

2. Malware classification and composition analysis: asurvey of recent developments;Abusitta;J. Inf. Secur. Appl.,2021

3. Network traffic classification using feature selections and two-tier stacked classifier;Adhao;Int. J. Next-Gener. Netw.,2021

4. Agilie. Top mobile security trends to watch in 2022. 2022. Available at https://agilie.com/blog/top-mobile-security-trends-to-watch-in-2020.

5. The comparison of cybersecurity datasets;Alshaibi;Data,2022

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1. Malware Detection with Artificial Intelligence: A Systematic Literature Review;ACM Computing Surveys;2024-01-22

2. Noisy Label Detection for Multi-labeled Malware;2024 IEEE 21st Consumer Communications & Networking Conference (CCNC);2024-01-06

3. TrustDroid: Enhancing Trust and Privacy for Android Mobile Phones by Preventing Riskware and SMS Malware;2023 2nd International Conference on Futuristic Technologies (INCOFT);2023-11-24

4. A Novel Mobile Malware Detection Model Based on Ensemble Learning;2023 Innovations in Intelligent Systems and Applications Conference (ASYU);2023-10-11

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