New hybrid approach boosts malware classification accuracy

By PulseAugur Editorial · [1 sources] · 2026-06-03 04:00

Researchers have developed a novel hybrid approach for malware classification, aiming to improve detection and mitigation efforts. The method fuses secondary features, including API calls and n-grams, using a customized selection process. A voting-based algorithm fusion is employed for the predictive model, achieving high accuracy with an AUC of 0.989 and 99.72% on a Microsoft dataset. AI

IMPACT This novel approach could enhance cybersecurity defenses by improving the accuracy and efficiency of malware family classification.

RANK_REASON The cluster contains an academic paper detailing a new methodology. [lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.AI TIER_1 English(EN) · Raja Khurram Shahzad, Muhammad Mustaqeem, Haroon Elahi · 2026-06-03 04:00

A Hybrid Approach For Malware Classification Using Secondary Features Fusion

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 classificatio…

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A Hybrid Approach For Malware Classification Using Secondary Features Fusion

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