Zero-Day Threat Mitigation via Deep Learning in Cloud Environments - Sebastian Ignacio Berrios Vasquez; Pamela Alejandra Hermosilla Monckton; Dante Ivan Leiva Muñoz; Hector Allende (Applied Sciences)
The growing sophistication of cyber threats has increased the need for advanced detection techniques, particularly in cloud computing environments. Zero-day threats pose a critical risk due to their ability to bypass traditional security mechanisms. This study proposes a deep learning model called mixed vision transformer (MVT), which converts binary files into images and applies deep attention mechanisms for classification. The model was trained using the MaLeX dataset in a simulated Docker environment. It achieved an accuracy between 70% and 80%, with better performance in detecting malware compared with benign files. The proposed MVT approach not only demonstrates its potential to significantly enhance zero-day threat detection in cloud environments but also sets a foundation for robust and adaptive solutions to emerging cybersecurity challenges.