arXiv:2607. 03653v1 Announce Type: cross Abstract: Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2610.01949v1 Announce Type: cross Abstract: Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are...
arXiv:2609.14437v1 Announce Type: cross Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability...
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:2609.14593v1 Announce Type: cross Abstract: Living-Off-the-Land (LOTL) is the dominant evasion technique of Advanced Persistent Threat (APT) actors, exploiting legitimate Windows utilities to c...
The paper introduces a new benchmark for assessing out-of-distribution robustness in graph-based Android malware classifiers, highlighting that current models drop up to 45% accuracy on unseen malware variants. It presents two scenarios—MalNet-Tiny-Common for covariate shift and MalNet-Tiny-Distinct for domain shift—and identifies a limitation in existing benchmarks that rely solely on structure-only function call graphs. To address this, the authors propose a semantic enrichment framework that augments graph topology with function-level attributes and LLM-based code embeddings, demonstrating that this data-centric approach improves robustness under distribution shift and complements model-based methods.
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.