Aug 2026· 2026 International Conference on Intelligent Multimedia, Networking, and Security (IMNS)· pp. 1-6· 0 citations· 19 references
Abstract
Vehicular Ad Hoc Networks (VANETs) rely on Basic Safety Messages (BSMs) to support safety-critical applications such as collision avoidance and traffic awareness. However, BSMs can be exploited in Sybil attacks, where adversaries generate multiple ghost vehicles to manipulate traffic conditions. In this work, we introduce Phantom Jam, a motion-consistent Sybil attack designed to induce large-scale traffic disruptions while maintaining temporally consistent vehicle behavior. Unlike traditional Sybil attacks that rely on deterministic motion patterns, Phantom Jam combines map-aware trajectory replay with generative temporal modeling. Specifically, TimeGAN is used to synthesize plausible braking and acceleration dynamics, enabling ghost vehicles to emulate natural driving behavior during slowdown and recovery phases. We evaluate Phantom Jam using the F2MD simulation framework and the LuST Nano traffic scenario, with a recent deep-learning-based misbehavior detection system as the benchmark. Our experimental results show that Phantom Jam can reduce recall to as low as 0.66, indicating that a substantial portion of malicious vehicles remain undetected. Our work demonstrates that plausible temporal dynamics in Sybil attacks can pose significant challenges for modern VANET misbehavior detection systems.
Vehicular Ad-Hoc Networks (VANETs) enable realtime communication for safety-critical applications including collision avoidance and traffic control. Their decentralized, dynamic architecture, however, makes them vulnerable to multiple attack classes, including Sybil, spoofing, Denial-of-Service (DoS), and other cyber threats. Existing defenses typically address cyber and physical layers independently, limiting their ability to capture the interplay between mobility patterns and attack propagation. This paper presents a cyber-physical simulation framework integrating vehicular mobility with the CybORG environment for multi-class attack mitigation. A Road Side Unit (RSU) acts as the infrastructure-based defender, monitoring vehicle behavior, maintaining trust scores, and executing defense actions via a Dueling Double Deep Q-Network with Prioritized Experience Replay (D3QN-PER). The agent learns optimal policies through environment interaction rather than static labeled data. Evaluation against two unsupervised baselines, Exponentially Weighted Moving Average (EWMA) and Trust-Gated Isolation Forest, demonstrates perfect detection performance (Recall = 100%, $\mathbf{F} \mathbf{1} \boldsymbol{=} \mathbf{1. 0 0 0 0})$ with zero false positives and zero false negatives, compared to 95.12% recall (EWMA) and 84.95% recall (Isolation Forest). The framework handles up to six concurrent attackers within the RSU's 200 m range with sub-millisecond latency, establishing a foundation for intelligent, adaptive security in vehicular networks.
Fasna Nadeera Irumpidamkandiyil Pocker, Farsana Ansari, Alexandre dos Santos Roque et al.· International Conference on...· 0 citations
: 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.· International Conference on...· 0 citations
Vehicular Ad-hoc Networks (VANETs) is a very basic form of Intelligent Transportation Systems (ITS), which enables the information exchange in real time among heterogeneous entities that includes vehicles, roadside units (RSUs), basic pedestrians, and also emergency vehicles. However, the open wireless medium, high node mobility, and absence of centralized infrastructure make VANETs highly susceptible to routing attacks such as blackhole attacks, denial-of-service (DoS) attacks, and node impersonation. Existing approaches largely assume static adversarial models and homogeneous network composition, limiting their practical applicability in dynamic road environments. This paper proposes a secure multi-strategy adaptive routing framework for heterogeneous VANET environments under dynamic adversarial conditions. The framework models the road network as an undirected dynamic graph derived from the California road network dataset (roadNet-CA) obtained from the Stanford Network Analysis Project (SNAP) repository. Node heterogeneity explicitly includes classification of network by having four different types that is RSU, vehicles, Pedestrians and also emergency vehicles. This uses a degree centrality-based assignment. The framework employs a mutual authentication mechanism that is having prior route which is defined and will be followed by a multi strategy secure routing engine. This mechanism evaluates by using four path strategies: Direct Avoidance, K-Shortest Path, Weighted Random Routing, and Node-Disjoint Routing. The proposed dynamic routing mechanism continuously monitors all the possible active path and then it tries to trigger and ensure the re-compute is done where the attacker is present. While doing the same analysis and computation we understand that the packet delivery ratio has improved and also there is a lot of energy saving. This method ensures to have reduced delay end to end and final stage the through was also compared the proposed method higher throughput.
M. Shilpa, V. Shilpa, P. Karthik et al.· Discover Computing· 0 citations
With the rapid advancement of vehicular communication technologies, maintaining reliable connectivity in Vehicular Ad Hoc Networks (VANETs) has become a critical challenge due to high mobility, dynamic topology, and uneven traffic distribution. Frequent disconnections in Vehicle-to-Vehicle (V2V) communication lead to increased latency and reduced network performance. To address these issues, this research proposes an AI-assisted dynamic Roadside Unit (RSU) deployment framework that leverages real-time traffic density estimation to optimize communication infrastructure. The proposed system utilizes deep learning-based vehicle detection models to analyze real-time traffic images and estimate vehicle density across different road segments. The extracted traffic information is further processed using machine learning techniques to predict communication demand and identify potential connectivity gaps. Based on these predictions, the system dynamically activates, deactivates, or repositions RSUs to ensure continuous network coverage and reduce dependency on unstable V2V links. The optimization model focuses on minimizing communication delay, enhancing packet delivery ratio, and improving overall network reliability through adaptive RSU placement. Additionally, a hybrid communication approach combining V2V and Vehicle-to-Infrastructure (V2I) is employed to overcome connectivity loss in sparse or highly dynamic traffic conditions. Simulation results demonstrate that the proposed AI-driven framework significantly improves network throughput, reduces communication latency, and ensures stable connectivity compared to traditional static RSU deployment strategies. The system effectively adapts to varying traffic patterns, making it suitable for next-generation intelligent transportation systems and smart city applications.
Sayyada Fahmeeda, Shashank, Jyoti et al.· International journal of com...· 0 citations
Communication delays induced by cyber attacks present a critical challenge to the safe operation of connected autonomous driving. This study investigates the use of intention sharing communication strategy to enhance the resilience of model predictive controllers under Denial-of-Service attacks. We employ a vehicle-in-the-loop testbed integrating a real drive-by-wire vehicle with a microscopic traffic simulator and vehicle-to-X communication infrastructure. We emulate Denial-of-service attacks that induce communication delays of up to two seconds. We evaluate three controller variants: baseline status-sharing, intention-sharing, and delay-aware intention-sharing control. Experimental results reveal that while baseline control suffers significant performance degradation and frequent collisions under adversarial delay, intention sharing eliminates collisions and maintains behavior near nominal levels for the tested scenarios. These findings demonstrate the practical potential of intention-sharing architectures for safeguarding connected vehicles against network-layer degradation.
Prakhar Gupta, Tyler Ard, Rong-Yao Wang et al.· 0 citations
The proposed Diff-DDoS framework, a three-phase framework for realistic attack synthesis and robust detection using tabular diffusion models, supports tabular diffusion models for stress-testing and hardening intrusion detectors in data-scarce 5G cyber-physical deployments.
Bilal Hussain, Xiao Tang, Qinghe Du et al.· 0 citations
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