This paper establishes a large deviation principle for the generalization error of interpolating classifiers in the overparametrized regime.

The study assesses the generalization capability of supervised machine learning models for intrusion detection using UNSW-NB15 and TON_IoT, finding a significant performance drop when models are teste…

This paper analyzes the poor performance of Meta-learning for Training-data Selection (MTS) and proposes that increasing the batch size and incorporating informative features can significantly improve…

The paper introduces PRAXIS, a novel algorithm that efficiently approximates the computation of 'Rashomon sets' for decision trees, significantly reducing memory and runtime complexity.

The paper proposes and validates a privacy-preserving framework using Homomorphic Encryption (HE) to train and run Machine Learning models on sensitive data while keeping it encrypted throughout the e…

This paper compares traditional machine learning models (Random Forests, XGBoost, SVM) against a complex Unified Multi-Task Time Series Model for churn prediction, concluding that conventional methods…

The paper introduces a new anytime-valid inference method to correct split selection in online decision trees, providing robust statistical guarantees for streaming data that existing methods lack.

The paper introduces a novel, non-deep neural network architecture that achieves the performance of LLMs by finding the global optimum of the loss function in a single, closed-form iteration, eliminat…

This paper introduces a novel malware detection system for macOS by utilizing domain-specific static features, achieving state-of-the-art performance and demonstrating strong generalization capabiliti…

The paper proposes a zero-label malware family classification framework that uses a weighted hierarchical ensemble of large language models (LLMs) to classify malware without requiring labeled trainin…

This study evaluates various data preprocessing pipelines to improve the transferability and generalization of Machine Learning models for detecting malware in Portable Executable (PE) files across di…

The paper proposes a robust, multi-stage pipeline combining rule-based classification and machine learning to map noisy retail product names to standardized consumption categories, finding that simple…

This paper introduces a method to automatically determine the optimal learning period ($ au$) for the Random Gradient hyper-heuristic, enabling it to optimally solve Pseudo-Boolean Problems without ma…

This paper compares lightweight machine learning models (like Random Forest) against computationally intensive deep learning methods for botnet detection on the CTU-13 dataset, showing that these simp…

The paper proposes DAMEL, a dual-axis multi-expert learning algorithm that simultaneously reduces both prediction bias and variance in class-imbalanced learning by leveraging multiple experts across b…

This paper proposes a hybrid feature fusion and voting-based approach for automated malware detection and classification into specific malware families, achieving high performance metrics like an AUC…

The paper introduces Trident, a novel malware detection system that combines static features, LLM-derived behavioral rules, and direct LLM analysis to achieve superior robustness against concept drift…

This paper analyzes how vulnerable various machine learning models are to data poisoning attacks in IoT intrusion detection, finding that ensemble methods are more robust than Logistic Regression and…

TIMEGATE introduces a resource-aware policy layer that manages continual ML adaptation by dynamically budgeting time and evaluation resources, achieving significant compute and energy savings without…

The paper proposes a structural method using decision tree rulesets and multiple complementary metrics to detect concept drift in evolving malware families, finding that fixed-interval windowing with…