Related papers: CITADEL: A Semi-Supervised Active Learning Framework for Malware Detection Under Continuous Distribution Drift

CITADEL: A Semi-Supervised Active Learning Framework for Malware Detection Under Continuous Distribution Drift

URL: http://arxiv.org/abs/2511.11979v1
Date: Sat, 15 Nov 2025 01:34:15 GMT
Title: CITADEL: A Semi-Supervised Active Learning Framework for Malware Detection Under Continuous Distribution Drift
Authors: Md Ahsanul Haque, Md Mahmuduzzaman Kamol, Ismail Hossain, Suresh Kumar Amalapuram, Vladik Kreinovich, Mohammad Saidur Rahman,
Abstract summary: We propose CITADEL, a robust semi-supervised active learning framework for Android malware detection.<n>We introduce malware-specific augmentations, Bernoulli bit flips and masking, that simulate realistic drift behaviors.<n>We show that CITADEL outperforms prior work, achieving F1 score of over 1%, 3%, 7%, and 14% respectively, using only 40% labeled samples.
Score: 2.6040598178524106
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Android malware evolves rapidly, leading to concept drift that degrades the performance of traditional machine learning (ML)-based detection systems. While recent approaches incorporate active learning and hierarchical contrastive loss to handle this drift, they remain fully supervised, computationally expensive, and perform poorly on real-world datasets with long temporal spans. In particular, our evaluation highlights these limitations, particularly on LAMDA, a 12-year longitudinal dataset exhibiting substantial distributional shifts. Moreover, manual expert labeling cannot scale with the daily emergence of over 450,000 new malware samples, leaving most samples unlabeled and underutilized. To address these challenges, we propose CITADEL, a robust semi-supervised active learning framework for Android malware detection. To bridge the gap between image-domain semi-supervised learning and binary feature representations of malware, we introduce malware-specific augmentations, Bernoulli bit flips and masking, that simulate realistic drift behaviors. CITADEL further integrates supervised contrastive loss to improve boundary sample discrimination and combines it with a multi-criteria active learning strategy based on prediction confidence, $L_p$-norm distance, and boundary uncertainty, enabling effective adaptation under limited labeling budgets. Extensive evaluation on four large-scale Android malware benchmarks -- APIGraph, Chen-AZ, MaMaDroid, and LAMDA demonstrates that CITADEL outperforms prior work, achieving F1 score of over 1%, 3%, 7%, and 14% respectively, using only 40% labeled samples. Furthermore, CITADEL shows significant efficiency over prior work incurring $24\times$ faster training and $13\times$ fewer operations.

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