A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution

Computer Science > Cryptography and Security

arXiv:2606.30572v1 (cs) [Submitted on 29 Jun 2026]

Title:A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution

Authors:Jithin S., Roshin Sleeba C., Anvin Mariya P. B., Asmitha K. A., Vinod P., Serena Nicolazzo, Antonino Nocera View a PDF of the paper titled A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution, by Jithin S. and 6 other authors View PDF HTML (experimental)

Abstract:Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families. Traditional single-model detection mechanisms often fail to generalize across such diverse data, leading to degraded performance, particularly on obfuscated and rare malware samples. In this work, we propose a unified multi-task malware analysis framework based on Mixture of Experts (MoE) architectures. The proposed system evaluates performance across two different input representations, i.e., high-dimensional EMBER feature sets and raw 1D byte arrays extracted from Portable Executable files. It simultaneously performs three critical tasks: malware family classification, packed versus unpacked detection, and malware versus benign identification. By decomposing the problem into specialized expert networks and employing adaptive gating mechanisms, the model enables effective task-specific learning while maintaining overall scalability. We investigate multiple architectural variants, including Homogeneous MoE, Heterogeneous MoE, and Multi-Gate MoE (MMoE). Performance is evaluated in both standard and adversarial settings using original and mutated samples. The obtained results demonstrate that the Multi-Gate MoE model achieves the best performance, reaching a combined detection rate of 0.9744 with only $2.56\%$ failure rate. Moreover, this configuration exhibits improved robustness under mutation-induced distribution shifts. Our findings highlight the effectiveness of expert specialization and task-specific routing in handling complex malware distributions, making the proposed framework a promising direction for scalable and resilient malware detection systems.

Subjects:	Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2606.30572 [cs.CR]
	(or arXiv:2606.30572v1 [cs.CR] for this version)
	https://doi.org/10.48550/arXiv.2606.30572 Focus to learn more arXiv-issued DOI via DataCite

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From: Serena Nicolazzo Dr [view email]
[v1] Mon, 29 Jun 2026 17:15:25 UTC (5,431 KB)
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