Proactive zero-day malware evolution forecasting via self-supervised multi-view contrastive representation learning and hybrid generative modeling

This publication has 44 references indexed in Scilit:

• Federated Contrastive Learning Framework for Proactive Malware Signature Generation in IoT Enabled Edge Environments
  EPJ Web of Conferences, 2025
• Zero day malware detection with Alpha: Fast DBI with Transformer models for real world application
  Computers and Electrical Engineering, 2025
• Leveraging the Power of Zero-Shot Learning for Malware Detection Using Application Programming Interface Call Sequences
  IEEE Access, 2025
• An Ensemble technique for imbalanced multiclass malware classification by leveraging API call semantics
  Discover Computing, 2025
• CAIMP: Cross-Architecture IoT Malware Detection and Prediction Based On Static Feature
  The Computer Journal, 2024
• Performance Evaluation of CNN and Pre-trained Models for Malware Classification
  Arabian Journal for Science and Engineering, 2023
• Malware‐SMELL: A zero‐shot learning strategy for detecting zero‐day vulnerabilities
  Computers & Security, 2022
• CNN based zero-day malware detection using small binary segments
  Forensic Science International: Digital Investigation, 2021
• Multi-view deep learning for zero-day Android malware detection
  Journal of Information Security and Applications, 2021
• L-RTAM: Logarithm based reliable trust assessment model for WBSNs
  Journal of Discrete Mathematical Sciences and Cryptography, 2021