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ViPER enhances malware detection by accounting for executable packing

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ViPER, a novel approach for malware detection that addresses the challenge of executable packing. ViPER utilizes a Vision Transformer (ViT) backbone adapted with LoRA, featuring a dual-head architecture to simultaneously classify malware and detect packing. A unique packing-aware gating mechanism allows for distinct predictions based on the inferred packing state, improving accuracy for both packed and unpacked binaries. The system achieved a balanced accuracy of 0.8521 and an ROC-AUC of 0.9260 on a dataset of 200,000 Windows PE byteplot images, outperforming existing state-of-the-art methods. AI

IMPACT This research could lead to more robust malware detection systems, particularly against evasion techniques like packing.

RANK_REASON The cluster describes a new research paper published on arXiv detailing a novel method for malware detection.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Fatima Qaiser, Bisma Tahir, Muhammad Abid Mughal, Nauman Shamim ·

    ViPER: Vision-based Packing-Aware Encoder for Robust Malware Detection

    arXiv:2606.12949v1 Announce Type: cross Abstract: Visualization-based malware detection maps raw binary bytes to grayscale images and applies learned visual classifiers, providing an evasion-resistant and disassembly-free alternative to conventional analysis pipelines. However, e…

  2. arXiv cs.CV TIER_1 English(EN) · Nauman Shamim ·

    ViPER: Vision-based Packing-Aware Encoder for Robust Malware Detection

    Visualization-based malware detection maps raw binary bytes to grayscale images and applies learned visual classifiers, providing an evasion-resistant and disassembly-free alternative to conventional analysis pipelines. However, executable packing remains a critical failure mode:…

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