Jul 2026· International Conference on Computer, Information and Telecommunication Systems· pp. 1-6· 0 citations· 12 references
Abstract
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.
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.
Colby Cook, Ahmed Mohamed, Mengjun Xie· 2026 International Conferenc...· 0 citations
Advanced Air Mobility (AAM) platforms rely on heterogeneous command and control (C2) links-LTE and SAT-COM-yet current frameworks prioritize connectivity over integrity, leaving them critically vulnerable to coordinated jamming, spoofing, and replay attacks. As AAM operations scale toward dense urban airspace, the absence of trust-aware link management introduces systemic risks that SNR-based switching policies are fundamentally ill-equipped to address. We propose a cognitive cyber-resilient C2 link management framework that elevates link trust above raw uptime by integrating multi-layered threat intelligence with adaptive decision-making. A hybrid BiLSTM+TCN anomaly detector achieves 89.69% accuracy with perfect recall $(\text{AUC} =1.000)$ across jamming and replay attack classes, enabling early and reliable threat identification under realistic channel conditions. Guided by dynamic threat probability estimates, RF fingerprint authentication, and a Watchdog safety layer, a Dueling Deep Q-Network (Dueling-DQN) agent selects defensive link-switching actions that preserve mission continuity without sacrificing security posture. The framework is implemented and validated in OMNeT++ 6.3 under five independent random seeds, demonstrating complete elimination of C2 hijack events across all single-vector attack scenarios. In contrast, the SNR-threshold baseline averaged two hijack events per scenario, confirming the inadequacy of reactive, signal-qualityonly approaches. These results establish a strong foundation for trust-centric, AI-driven C2 link resilience in next-generation AAM deployments.
Syed Ali Raza, Muhammad Wisal, Yichen Shao et al.· 2026 6th International Confe...· 0 citations
The potential of the EMAO-MTRUST protocol to secure VANETs against malicious activities while maintaining high-quality performance is highlighted, making it a promising solution for the challenges in ITS applications.
Parimala Garnepudi, M. Vanitha· Tehnički Vjesnik· 0 citations
Vehicular Ad Hoc Networks (VANETs) play a critical role in Intelligent Transportation Systems (ITS) by enabling real-time vehicle communication for safety and traffic management. However, the open and decentralized nature of VANETs makes them vulnerable to False Information Attacks (FIA), where malicious vehicles disseminate fabricated data such as fake congestion alerts or incorrect speed information. This paper presents a lightweight and infrastructure-free framework for detecting FIA using an unsupervised machine learning approach based on the Isolation Forest algorithm. Unlike existing methods that require roadside units (RSUs), labeled datasets, or computationally intensive network simulators, the proposed framework operates using a small set of behavioral features extracted from vehicle beacon messages. The system is implemented entirely in Python and evaluated on a synthetically generated dataset designed to emulate realistic VANET conditions. Experimental results demonstrate an accuracy of 94.2%, precision of 86.1%, recall of 77.5%, and an F 1 -score of 81.6%. The results show that the proposed framework achieves competitive detection performance while maintaining low computational overhead, making it suitable for deployment on resource-constrained onboard units (OBUs) in real-world vehicular networks.
Hassan Mistareehi, Shafika Showkat Moni, A. Aboualy et al.· International Conference on...· 0 citations
Cloud-enabled Intelligent Transportation Systems (ITS) leverage Vehicle-to-Everything (V2X) communications to support scalable data processing and real-time traffic management. However, this integration significantly expands the cyber-physical attack surface. Conventional intrusion detection systems (IDSs) that rely on static signatures or offline-trained models are often ill-suited to counter adaptive attackers. This paper presents the Adaptive Stackelberg Defense Scheme (ASDS), a proactive intrusion detection system that models attacker-defender interactions as a hierarchical Bayesian Stackelberg game with incomplete information. ASDS employs Bayesian filtering to jointly estimate system states and attacker types in real time, enabling adaptive defense strategies. Evaluated against False Data Injection (FDI), Denial-of-Service (DoS), and spoofing attacks, ASDS achieves detection accuracy between 94% and 98%, false positive rates ranging from 0.02 to 0.08, and response latency under 50 ms. These results underscore its effectiveness in securing cloud-enabled ITS environments.
Emmanuel Kigmo Yonga, Mounirah Djam-Doudou, J. Emati et al.· 2026 6th International Confe...· 0 citations
This paper presents a comprehensive and systematic review of deep learning techniques applied to cyber intrusion detection within IoV systems, conducted in accordance with the PRISMA framework across 83 selected studies published between 2020 and 2025.
Duygu Kayaoğlu, Eyup Emre Ulku, Onder Demir· Journal of Supercomputing· 0 citations