RPM-Net Reciprocal Point MLP Network for Unknown Network Security Threat Detection

Computer Science > Cryptography and Security [Submitted on 8 Apr 2026] RPM-Net Reciprocal Point MLP Network for Unknown Network Security Threat Detection Jiachen Zhang, Yueming Lu, Fan Feng, Zhanfeng Wang, Shengli Pan, Daoqi Han 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:this https URL Comments: Compared to the ICASSP 2026 proceedings version, this version corrects a transcription error in Table 1 (ODIN's precision, recall, and f1 scores) Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.06638 [cs.CR]   (or arXiv:2604.06638v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.06638 Focus to learn more Submission history From: Jiachen Zhang [view email] [v1] Wed, 8 Apr 2026 03:25:43 UTC (341 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)