arXiv:2606.03432v1 Announce Type: cross Abstract: The number of malware (either variant or novel) is rapidly increasing, making malware detection and mitigation a complex problem. One approach to improving malware mitigation is automatic detection and malware family classification. However, traditional malware detection methods cannot classify detected malware into their respective families, hindering effective malware mitigation. Consequently, this paper proposes a method to automate malware detection and classification of the detected malware into respective malware families. The proposed me
The rapid increase in malware variants and novel threats necessitates more sophisticated, automated detection and classification methods to keep pace with cyber adversaries.
Effective malware classification is crucial for maintaining cybersecurity integrity across all digital infrastructure, protecting critical systems and intellectual property from increasingly complex attacks.
This research contributes to advancing automated malware analysis, potentially enabling faster and more accurate identification of threats and their origins, improving defensive postures.
- · Cybersecurity firms
- · Organizations with advanced threat detection needs
- · National security agencies
- · Malware developers
- · Organizations relying on outdated defense mechanisms
Improved malware classification leads to quicker threat response and reduced incident impact for targeted organizations.
Enhanced automated defenses could free up human cybersecurity analysts for more strategic threat intelligence activities.
A significant reduction in successful malware attacks could shift the balance of power in cyber warfare, increasing the cost and complexity for state-sponsored and criminal actors.
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