A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution
arXiv:2606.30572v1 Announce Type: cross 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 repre
Malware sophistication continues to increase, demanding more robust and adaptive detection mechanisms, which AI, specifically MoE architectures, is now capable of providing.
This development enhances cybersecurity defenses, particularly against advanced and evasive malware, protecting critical infrastructure and intellectual property.
The ability to accurately classify and attribute malware, even packed and obfuscated samples, significantly improves response capabilities and reduces attacker dwell time.
- · Cybersecurity firms
- · Enterprise IT departments
- · Government agencies
- · Malware developers
- · Cybercrime syndicates
- · Nation-state hacking groups
Improved detection rates for sophisticated malware will reduce successful cyberattacks.
The cost of developing and deploying effective malware will increase, potentially shifting attacker tactics.
Enhanced cybersecurity could contribute to greater stability in digital infrastructure and reduce economic losses from cybercrime.
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Read at arXiv cs.AI