arXiv Machine Learning
Jul 13
Regression-aware Continual Learning for Android Malware Detection
arXiv:2507. 18313v2 Announce Type: replace Abstract: Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated.
Concept Drift Detection and Adaptive Retraining of Malware Classification Models
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.
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arXiv Machine Learning
Jun 24
SEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget
arXiv:2605. 24903v2 Announce Type: replace-cross Abstract: Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications.
arXiv AI
Jun 19
Learner-based Concept Drift Detection: Analysis and Evaluation
arXiv:2606. 20216v1 Announce Type: cross Abstract: Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift.
arXiv Machine Learning
Aug 5
On the Performance of Malware Detection Classifiers Using Hardware Performance Counters
arXiv:2608. 02671v1 Announce Type: cross Abstract: Malware detection using Hardware Performance Counters (HPC) has emerged as a promising solution to improve the security of computing systems as a complement to antivirus software.
arXiv Machine Learning
Jun 9
A Framework for Evaluating and Benchmarking Concept Drift Detection Methods
arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.
arXiv Machine Learning
Aug 5
NOMADD: Numerical Optimization of Models Adapting to Data Drift
arXiv:2608. 02845v1 Announce Type: new Abstract: Tabular model performance degrades when feature distributions change over time or the relationship between features and outcome variables change over time, known as data drift and concept drift, respectively.
arXiv AI
Jun 3
High-Precision APT Malware Attribution with Out-of-Scope Resilience
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 Machine Learning
Jul 21
When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift
arXiv:2602. 14161v2 Announce Type: replace Abstract: Detecting prompt injection, jailbreak attacks, and harmful requests is critical for deploying LLM-based agents safely, yet current evaluation practices in this literature overestimate generalization.
arXiv AI
1d ago
Retrofit: Continual Learning with Controlled Forgetting for Binary Security Detection and Analysis
arXiv:2511. 11439v3 Announce Type: replace-cross Abstract: Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics.
arXiv AI
Jun 30
A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution
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 AI
Jun 8
Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs
arXiv:2601. 12359v1 Announce Type: cross Abstract: Prompt injection attacks have become an increasing vulnerability for LLM applications, where adversarial prompts exploit indirect input channels such as emails or user-generated content to circumvent alignment safeguards and induce harmful or unintended outputs.
arXiv Machine Learning
Jul 20
Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications
arXiv:2508. 00042v2 Announce Type: replace-cross Abstract: Machine learning models deployed in non-stationary environments degrade silently, since as the input distribution drifts their accuracy decays without an error signal and without labels to reveal it.