RPM-Net learns reciprocal points for each known attack class plus adversarial constraints to detect unknown threats, with RPM-Net++ adding Fisher regularization, and reports better F1, AUROC, and AUPR-OUT than prior methods.
RPM-Net Reciprocal Point MLP Network for Unknown Network Security Threat Detection
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abstract
Effective detection of unknown network security threats in multi-class imbalanced environments is critical for maintaining cyberspace security. Current methods focus on learning class representations but face challenges with unknown threat detection, class imbalance, and lack of interpretability, limiting their practical use. To address this, we propose RPM-Net, a novel framework that introduces reciprocal point mechanism to learn "non-class" representations for each known attack category, coupled with adversarial margin constraints that provide geometric interpretability for unknown threat detection. RPM-Net++ further enhances performance through Fisher discriminant regularization. Experimental results show that RPM-Net achieves superior performance across multiple metrics including F1-score, AUROC, and AUPR-OUT, significantly outperforming existing methods and offering practical value for real-world network security applications. Our code is available at:https://github.com/chiachen-chang/RPM-Net
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cs.CR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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RPM-Net Reciprocal Point MLP Network for Unknown Network Security Threat Detection
RPM-Net learns reciprocal points for each known attack class plus adversarial constraints to detect unknown threats, with RPM-Net++ adding Fisher regularization, and reports better F1, AUROC, and AUPR-OUT than prior methods.