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Malware Detection Using RNA Encoding and Convolutional Neural Networks on the Malicious Network Dataset [version 3; peer review: 2 approved with reservations]

Name: Malware Detection Using RNA Encoding and Convolutional Neural Networks on the Malicious Network Dataset [version 3; peer review: 2 approved with reservations]
Start: 2026-06-03T14:49
End: 2026-06-03T14:49
Location: Local events

Business & Networking

When:: June 3, 2026 · 2:49 PM
Source:: F1000Research

Background The detection of malware in network traffic remains a critical cybersecurity challenge. Traditional signature-based intrusion detection demonstrates a high level of familiarity with issues that have been recorded in the database; but show significantly lower effectiveness when it comes to polymorphic or zero-day attacks. Conversely, anomaly-based approaches are also endowed with the ability to detect new incursions, but often have a high false-positive rate. Methods This study propose

Malware Detection Using RNA Encoding and Convolutional Neural Networks on the Malicious Network Dataset [version 3; peer review: 2 approved with reservations]

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