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Yun-Peng Zhang

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Open access 2026

Modeling and Mitigating Physical Layer Jamming in Connected Vehicular Networks

: Connected vehicles rely on Vehicle to Everything (V2X) communication to enable safety critical and cooperative driving applications. The open nature of wireless channels makes these systems vulnerable to physical layer jamming attacks, which can disrupt message exchange and potentially compromise traffic safety. In this paper, we present an integrated co-simulation framework that models jamming attacks in connected vehicular networks using OMNeT++, SUMO, and Veins. We emulate realistic barrage style jamming through protocol compliant interference at the IEEE 802.11p physical layer and evaluate its impact on key communication metrics including Packet Delivery Ratio (PDR), latency, and channel utilization. We further implement Frequency Hopping Spread Spectrum (FHSS) as a mitigation strategy and assess its effectiveness under sustained interference. Our results show that a single 200 mW jammer reduces PDR from 100% to 9.1%, and that FHSS reduces SNIR corrupted frames by 63.3% while simultaneously suppressing jammer throughput by 49%. Beyond the security evaluation, this work contributes a modular, reproducible simulation framework explicitly designed to support the vehicular security research community.

Kumoulica Allu, Yun-Peng Zhang, Chang-Qing Luo et al. · 0 citations
Open access 2026

TAE-MAGSAGE: Topology Aware Metric Learning for Graph Based Network Intrusion Detection

TAE-MAGSAGE, an edge-centric graph learning model which uses the observed communication structure to construct graphs and applies a line graph transformation to perform flow-level classification without collapsing the interaction relationships, is introduced.

Poonam Nehru, Yunpeng Zhang, Renjie Hu et al. · 0 citations

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