The paper examines whether modern drift‑adaptive malware classifiers provide reliable and stable confidence estimates when faced with distribution shifts. It introduces Aurora, a framework that evaluates confidence quality and operational resilience, and presents metrics that extend beyond point‑in‑time performance to assess stability over time. The study finds that state‑of‑the‑art models exhibit fragility across datasets with varying drift severity, indicating a need to revisit underlying assumptions.
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.
arXiv:2507. 18313v2 Announce Type: replace Abstract: Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated.
arXiv:2608. 13465v1 Announce Type: cross Abstract: Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model.
arXiv:2503.11841v2 Announce Type: replace-cross Abstract: Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as truste...
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.
Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security analysts rely on to understand and triage alerts. We extend our prior MLP conference study to Random Forest and XGBoost across four tabular security datasets (phishing URLs, UNSW-NB15, NF-ToN-IoT, HIKARI-2021), evaluating five attacks including three black-box methods applicable to non-differentiable tree models.
arXiv:2607. 01679v1 Announce Type: cross Abstract: Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security analysts rely on to understand and triage alerts.
The paper addresses the challenge of adapting malware detection systems to new threats without retraining from scratch, focusing on the Few-Shot Class-Incremental Learning (FSCIL) setting. It proposes a hybrid framework that uses a self-supervised learning backbone pre-trained on malware packets, incorporates Low-Rank Adaptation (LoRA) to adapt the model while preserving core representations, and employs a prototype-based classification head for incremental sessions. Experiments on multiple datasets show that this approach consistently outperforms existing FSCIL baselines and achieves state-of-the-art performance.
arXiv:2606. 30572v1 Announce Type: cross Abstract: Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families.
arXiv:2607. 05481v1 Announce Type: cross Abstract: Detection models running in adversarial environments face a malicious distribution that drifts rapidly while the benign distribution stays comparatively stable, so teams retrain and redeploy constantly to stay ahead of new threats.
The paper evaluates three AI model security scanners—ModelScan, ModelAudit, and Fickling—using a benchmark of 170 Pickle and PyTorch artifacts from 145 families, 135 of which have binary security labels. It distinguishes coverage metrics such as non‑N/A coverage, analysis completion, and definitive security decisions, finding that ModelAudit achieved 100% definitive decisions, Fickling 81.5%, and ModelScan 49.6%. When a definitive judgment was made, ModelScan reached perfect precision, recall, and F1, while Fickling added no unique true positives beyond those found by the other tools.
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 %.