The paper presents a lightweight machine‑learning approach for multi‑class malware detection on resource‑constrained devices. Using a LightGBM classifier with SMOTE oversampling, SOM‑US undersampling, and Genetic‑Algorithm feature selection, the authors achieve 89.1 % accuracy on four malware families and 76 % on 16 individual malware types. A second Random‑Forest model further improves family classification to 91.2 % and individual classification to 78.7 %.
By Abdul Khalek Alve, Alif Rahman, Saadman Zaman, Sazzad Hossen Himel, Muhammad Iqbal Hossain
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...
By Emmanuela Andam, Yasir Abbas Zaidi, Abdelali Hadir, Emmanuel Grant, Naima Kaabouch
arXiv:2607. 24177v1 Announce Type: cross Abstract: Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since existing evaluations (i) differ in terms of data used for both training and testing; (ii) do not consider temporal analysis to showcase whether models withstand the passage of time; (iii) avoid security evaluations with adversarial attacks that could highlight their brittleness against content-injection attacks; and (iv) neglect the computational requirements for deployment, risking slow inference on endpoints.
By Andrea Ponte, Daniel Gibert, Matous Kozak, Dmitrijs Trizna, Maura Pintor, Battista Biggio, Fabio Roli, Luca Demetrio
arXiv:2608. 19901v1 Announce Type: cross Abstract: Agent Skills extend LLM agents with reusable instruction packages that may also include scripts, resources, and service configuration.
By Yue Wang, Yi Liu, Gelei Deng, Ying Zhang, Yuekang Li, Zhenyu Chen, Leo Zhang
arXiv:2606. 03523v1 Announce Type: cross Abstract: Early attribution of Advanced Persistent Threat (APT) activity can help defenders prioritise investigation, select countermeasures, and reduce the impact of an intrusion.
By Peter Williams, Adam Sobey, Erisa Karafili
Delphi Scanner is a static malware detection system for Windows PE files that balances efficiency and interpretability. It employs a convolutional neural network to model Windows API sequences and a rule‑based interpretation layer to map APIs to high‑level malicious capabilities. Tested on over 190,000 PE files, it achieves 95.35% accuracy with a 1.53 MB model, and demonstrates robustness against out‑of‑distribution samples and adversarial manipulations.
By Bijied Brahimi, Vincent Cohadon, Gabriel Glazman, Rayan Al Mohaize, Omran Berjawi, Rida Khatoun