CoMeT-Net: Consensus Memory Template Network for Real-time Traffic Anomaly Detection

[2608.14576] CoMeT-Net: Consensus Memory Template Network for Real-time Traffic Anomaly Detection Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Networking and Internet Architecture arXiv:2608.14576 (cs) [Submitted on 10 Jun 2026] Title: CoMeT-Net: Consensus Memory Template Network for Real-time Traffic Anomaly Detection Authors: Tingcong Jiang , Adhwaa Alchaab , Ayman Younis , Dario Pompili View a PDF of the paper titled CoMeT-Net: Consensus Memory Template Network for Real-time Traffic Anomaly Detection, by Tingcong Jiang and 3 other authors View PDF HTML (experimental) Abstract: Real-time anomaly detection in Open Radio Access Networks (O-RAN) demands high accuracy, low false alarms, and computational efficiency for resource-constrained edge deployment. Traditional methods struggle with computational overhead, inconsistent cross-domain performance, and suboptimal feature representations that miss subtle attacks on O-RAN's open interfaces. We present CoMeT-Net (Consensus Memory Template Network), a framework achieving state-of-the-art detection through three innovations: (1) structured memory banks enabling template-based consensus voting with $O(N \cdot C)$ complexity; (2) adaptive gating that downweights ambiguous features as a learned noise filter; (3) contrastive alignment unifying feature learning and classification. Deployed in O-RAN infrastructure via edge servers and Near-RT RIC xApp, CoMeT-Net enables dynamic threat mitigation through PRB throttling and RRC connection release. On network traffic datasets, CoMeT-Net achieves 99.35% F1 score with 10$\times$ lower false alarm rates than baselines while maintaining 0.3-3ms inference across hardware tiers from servers to Raspberry Pi 4. O-RAN testbed validation demonstrates effective isolation, degrading attacker latency to >1400ms while preserving 15-20ms for legitimate users. Comments: Accepted for publication in the proceedings of the IEEE International Conference on Sensing, Communication, and Networking (SECON), Pisa, Italy, July 2026. Recipient of the Best Student Paper Award Subjects: Networking and Internet Architecture (cs.NI) Cite as: arXiv:2608.14576 [cs.NI] (or arXiv:2608.14576v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2608.14576 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Adhwaa Alchaab [ view email ] [v1] Wed, 10 Jun 2026 23:33:50 UTC (17,264 KB) Full-text links: Access Paper: View a PDF of the paper titled CoMeT-Net: Consensus Memory Template Network for Real-time Traffic Anomaly Detection, by Tingcong Jiang and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.NI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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