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Performance Evaluation Data Selection Strategy
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Abstract
Artificial intelligence and machine learning have significantly advanced malware research by enabling automated threat detection and behavior analysis. However, the availability of exploitable data is limited, due to the absence of large datasets with real-world data. Despite the progress of AI in cybersecurity, malware analysis still suffers from this data scarcity, which limits model generalization. In order to tackle this difficulty, this workinvestigates TabPFN, a learning-free model designed for low-data regimes. We evaluate its performance against established baselines such as Random Forest, LightGBM and XGBoost, across multiple class configurations. Our experimental results indicate that TabPFN surpasses all other models in low-data regimes, with a 2