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Optimal Weighted Voting-Based Collaborated Malware Detection for Zero-Day Malware: A Case Study on VirusTotal and MalwareBazaar

Author

Listed:
  • Naonobu Okazaki

    (Faculty of Engineering, University of Miyazaki, 1-1 Gakuen-Kibanadai-Nishi, Miyazaki-shi 889-2192, Miyazaki, Japan
    These authors contributed equally to this work.)

  • Shotaro Usuzaki

    (Faculty of Engineering, University of Miyazaki, 1-1 Gakuen-Kibanadai-Nishi, Miyazaki-shi 889-2192, Miyazaki, Japan
    These authors contributed equally to this work.)

  • Tsubasa Waki

    (Faculty of Engineering, University of Miyazaki, 1-1 Gakuen-Kibanadai-Nishi, Miyazaki-shi 889-2192, Miyazaki, Japan)

  • Hyoga Kawagoe

    (Faculty of Engineering, University of Miyazaki, 1-1 Gakuen-Kibanadai-Nishi, Miyazaki-shi 889-2192, Miyazaki, Japan)

  • Mirang Park

    (Faculty of Information Technology, Kanagawa Institute of Technology, 1030 Shimo-Ogino, Atsugi-shi 243-0292, Kanagawa, Japan)

  • Hisaaki Yamaba

    (Faculty of Engineering, University of Miyazaki, 1-1 Gakuen-Kibanadai-Nishi, Miyazaki-shi 889-2192, Miyazaki, Japan)

  • Kentaro Aburada

    (Faculty of Engineering, University of Miyazaki, 1-1 Gakuen-Kibanadai-Nishi, Miyazaki-shi 889-2192, Miyazaki, Japan)

Abstract

We propose a detection system incorporating a weighted voting mechanism that reflects the vote’s reliability based on the accuracy of each detector’s examination, which overcomes the problem of cooperative detection. Collaborative malware detection is an effective strategy against zero-day attacks compared to one using only a single detector because the strategy might pick up attacks that a single detector overlooked. However, cooperative detection is still ineffective if most anti-virus engines lack sufficient intelligence to detect zero-day malware. Most collaborative methods rely on majority voting, which prioritizes the quantity of votes rather than the quality of those votes. Therefore, our study investigated the zero-day malware detection accuracy of the collaborative system that optimally rates their weight of votes based on their malware categories of expertise of each anti-virus engine. We implemented the prototype system with the VirusTotal API and evaluated the system using real malware registered in MalwareBazaar. To evaluate the effectiveness of zero-day malware detection, we measured recall using the inspection results on the same day the malware was registered in the MalwareBazaar repository. Through experiments, we confirmed that the proposed system can suppress the false negatives of uniformly weighted voting and improve detection accuracy against new types of malware.

Suggested Citation

  • Naonobu Okazaki & Shotaro Usuzaki & Tsubasa Waki & Hyoga Kawagoe & Mirang Park & Hisaaki Yamaba & Kentaro Aburada, 2024. "Optimal Weighted Voting-Based Collaborated Malware Detection for Zero-Day Malware: A Case Study on VirusTotal and MalwareBazaar," Future Internet, MDPI, vol. 16(8), pages 1-16, July.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:8:p:259-:d:1441218
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    References listed on IDEAS

    as
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