Journal Issues

Abstract- Malware detection increasingly requires advanced methodologies as evasion mechanisms outpace traditional defenses. This survey examines deep learning's application to malware detection, analyzing both achievements and fundamental limitations. Deep learning substantially improves detection performance through convolutional networks for code analysis, recurrent networks for behavioral patterns, and multi-modal fusion integrating complementary signals. Transfer learning enables effective knowledge transfer with limited labeled data. However, adversarial examples evade detection while preserving functionality, concept drift causes persistent accuracy degradation as malware evolves adaptively, and zero-day detection remains substantially less effective than known malware detection. These vulnerabilities reflect fundamental constraints rather than technical limitations. The attack-defense asymmetry creates inherent offensive advantages impossible to overcome technologically. The accuracy-robustness trade-off prevents simultaneous optimization due to underlying mathematical properties. Concept drift operates as persistent adversarial adaptation exploiting detected weaknesses. The interpretability-accuracy paradox creates tension between performance and regulatory transparency requirements. Advancing the field requires theoretical research pursuing fundamental robustness, organizational implementation of coordinated multi-layer defenses with human-AI collaboration, and policy frameworks establishing realistic expectations with international coordination. This review concludes that deep learning achieves genuine improvements while introducing novel vulnerabilities, that fundamental constraints limit further progress, and that pragmatic security must acknowledge theoretical impossibility of perfect prevention while pursuing continuous improvement through rapid detection and response.

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