arXiv:2607. 03653v1 Announce Type: cross Abstract: Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2608. 11634v1 Announce Type: cross Abstract: Dropout regularization is commonly used to reduce overfitting by removing parts of a neural network during training.
arXiv:2606. 03432v1 Announce Type: cross Abstract: The number of malware (either variant or novel) is rapidly increasing, making malware detection and mitigation a complex problem.
arXiv:2604. 17376v2 Announce Type: replace-cross Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods.
arXiv:2606. 30572v1 Announce Type: cross Abstract: Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families.
arXiv:2511. 07210v3 Announce Type: replace-cross Abstract: Clean-image backdoor attacks, which use only label manipulation in training datasets to compromise deep neural networks, pose a significant threat to security-critical applications.
arXiv:2606. 06570v1 Announce Type: cross Abstract: Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve.