arXiv:2512. 20872v2 Announce Type: replace-cross Abstract: Function call graphs (FCGs) have emerged as a powerful abstraction for malware detection, capturing the behavioral structure of applications beyond surface-level signatures.
By Jakir Hossain, Jue Guo, Gurvinder Singh, Lukasz Ziarek, Ahmet Erdem Sar{\i}y\"uce
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
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
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:2607. 03350v1 Announce Type: cross Abstract: Malicious Python packages have become a major threat to software supply chain ecosystems due to the widespread adoption of open-source repositories such as PyPI.
By Hang Gao, Xiaoyu Chen, Baoquan Cui, Zhen Tang, Peng Qiao, Fengge Wu, Jian Zhang
arXiv:2510. 26307v3 Announce Type: replace-cross Abstract: Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience.
By Laura Jiang, Reza Ryan, Qian Li, Nasim Ferdosian