arXiv:2606. 05584v1 Announce Type: cross Abstract: High-dimensional feature representations are widely used in machine learning-based cyberattack detection systems.
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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...
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 %.
arXiv:2606. 05714v1 Announce Type: cross Abstract: Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors including healthcare, finance, transportation, energy and government systems is growing.
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:2606. 09934v1 Announce Type: new Abstract: Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in operational and defense networks.
arXiv:2609.36039v1 Announce Type: cross Abstract: Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing s...