Comparative Review of Deep Learning Models for Malware Detection Under Adversarial Concept Drift
Abstract
Malware writers keep changing their programs so that security tools cannot catch them. Deep- learning detectors may work very well in the laboratory but become less reliable when they meet new and changed threats in real life. This study presents a comparative review of deep-learning models for malware detection under adversarial concept drift. Adversarial concept drift means deliberate changes in the nature or behaviour of malware that slowly reduce the reliability of a detector. Using a structured review method, the study searched digital libraries, conference archives and a preprint server, coded 20 studies published between 2018 and 2025, and checked their quality with six questions. It then compared nine families of deep-learning models and seven adaptation strategies using an ordinal rating on six criteria. Eighteen studies passed the quality threshold and formed the core set. Fifteen of the 20 studies tested their models over time, but only three described an attacker, and none combined deliberate attack with testing over time. Evidence on drift for deep models exists only for feed-forward networks, one autoencoder or contrastive encoder, and one graph neural network, so five of the nine families have no such evidence. Feed- forward networks ranked first under equal weighting (3.33), while graph neural networks ranked first when robustness against attack was counted twice (3.29). Drift detection (3.67) and feature stabilisation (3.50) were the best adaptation strategies. How well a model recovers after a long deliberate attack has not been tested. The findings favour methods that need few labels and that are tested over time, and this is useful for organisations in Nigeria.
References
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