Image-based malware representation approach with EfficientNet convolutional neural networks for effective malware classification-Reference-Cited by-同舟云学术

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Image-based malware representation approach with EfficientNet convolutional neural networks for effective malware classification

Published:2022-09 Issue: Volume:69 Page:103306
ISSN:2214-2126
Container-title:Journal of Information Security and Applications
language:en
Short-container-title:Journal of Information Security and Applications

Author:

Chaganti Rajasekhar,Ravi Vinayakumar^ORCID,Pham Tuan D.

Publisher

Elsevier BV

Subject

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

Reference36 articles.

1. Solarwinds attack explained: And why it was so hard to detect — CSO online;Constantin,2020

2. Kaseya ransomware attack updates: Your questions answered — ZDNet;Osborne,2021

3. Malware statistics in 2021: Frequency, impact, cost & more;Cook,2021

4. Number of malware attacks per year 2020 — Statista;Johnson,2021

5. A comprehensive review on malware detection approaches;Aslan;IEEE Access,2020

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1. Deep hybrid approach with sequential feature extraction and classification for robust malware detection;Egyptian Informatics Journal;2024-09

2. Efficient malware detection using hybrid approach of transfer learning and generative adversarial examples with image representation;Expert Systems;2024-08-13

3. API2Vec++: Boosting API Sequence Representation for Malware Detection and Classification;IEEE Transactions on Software Engineering;2024-08

4. Image-based detection and classification of Android malware through CNN models;Proceedings of the 19th International Conference on Availability, Reliability and Security;2024-07-30

5. SoK: Visualization-based Malware Detection Techniques;Proceedings of the 19th International Conference on Availability, Reliability and Security;2024-07-30

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