arXiv:2607. 26634v1 Announce Type: cross Abstract: Organizational digitalization expands cybersecurity risks, making cybersecurity an increasingly important research area in Information Systems (IS).
By Yinan Gao, Jiarong Xu, Xiaohang Zhao, Xiao Fang
arXiv:2606. 07792v1 Announce Type: cross Abstract: MOLOT (Malicious Operational Logic Observation Transformer) is a static malicious-code detection system designed for SAST setup where package metadata, maintainer history, and dynamic execution traces may be unavailable or unreliable.
By Daniil Lopatkin, Maksim Mitrofanov, Stanislav Rakovsky, Aleksandr Khalikov
arXiv:2509. 14335v2 Announce Type: replace-cross Abstract: Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them with code evidence.
By Xinran Zheng, Xingzhi Qian, Yiling He, Shuo Yang, Lorenzo Cavallaro
arXiv:2604. 04977v2 Announce Type: replace-cross Abstract: Software supply chain security compromises often stem from cascaded interactions of vulnerabilities, for example, between multiple vulnerable components.
By Laura Baird, Armin Moin
arXiv:2607. 19674v1 Announce Type: cross Abstract: Federated Graph Neural Networks (FedGNNs) are highly vulnerable to backdoor poisoning, yet existing defenses typically rely on rule-based approaches that lack semantic understanding, making them vulnerable to stealthy triggers and harmful to benign structures.
By Chenyu Zhou, Yabin Peng, Wei Huang, Kunlin Li, Shuaishuai Zhang, Xinyuan Miao
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.
By Ngoc N. Tran, Anwar Said, Waseem Abbas, Tyler Derr, Xenofon D. Koutsoukos