A paper from Prof. Dario Pompili's CPS Lab received the Student Best Paper Award at IEEE SECON’26.
The paper is titled "CoMeT-Net: Consensus Memory Template Network for Real-time Traffic Anomaly Detection” and is co-authored by Tingcong Jiang, Adhwaa Alchaab, Ayman Younis, and Dario Pompili. Besides being very proud of my co-authors and their work, I want to congratulate another PhD student from my lab, Zhile Li, who—beside presenting his own paper—presented this paper as well on behalf of the authors who could not travel internationally. He must have presented it very well to earn them the award!
Congratulations to Dario and the students!
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· 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× 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.
