A Spatiotemporal, Congestion-Context-Aware Vehicular Communication Dataset for Malicious Flooding Attack Analysis

This dataset presents a comprehensive spatiotemporal representation of vehicular communication under benign and malicious flooding conditions, simulated across both highway and metropolitan mobility environments. It contains twelve flow-level datasets, each representing one of three calibrated malicious flooding attack intensities (LOW, MEDIUM, HIGH) under the two mobility scenarios. The dataset is intended to support research on spatiotemporal anomaly detection, congestion-context-aware security, and machine-learning-based intrusion detection in Vehicular Communication Networks (VCNs).

Each data instance corresponds to a single unidirectional communication flow between a transmitting and receiving vehicle. The dataset includes rich temporal features (start time, end time, duration, temporal mid-point), spatial features (positions, mid-points, distances), mobility features (radial displacement and velocity), and communication-layer metrics such as packet and byte counts, throughput, delay, jitter, hop count, delivery ratio, and loss indicators. These combined descriptors capture how movement, network load, and communication dynamics evolve jointly over time.

Malicious flooding scenarios were simulated within the communication environment, with attackers transmitting elevated packet or byte volumes while remaining fully consistent with their underlying mobility behavior. This ensures that malicious flows do not exhibit unrealistic spatial artefacts and cannot be trivially separated from benign activity based solely on positional features. The resulting communication patterns provide challenging and realistic attack conditions that require context-aware and temporally informed detection methods. Full details of the flooding attack design and simulation methodology are provided in the associated research publication.

This release contains only the flow-level datasets. To enable higher-level analysis, we additionally provide Jupyter notebooks demonstrating how to transform flows into (i) session-level representations (aggregating all flows exchanged between a source–destination pair) and (ii) receiver-window representations (aggregating flows received by each node within a cluster). These notebooks also illustrate how to derive congestion-aware contextual features used in our associated research (see citation in documentation).

By combining realistic vehicle mobility, detailed communication metrics, calibrated attack intensities, and extensible multi-level processing scripts, this dataset offers a comprehensive foundation for research on spatiotemporal security, anomaly modelling, congestion-aware intrusion detection, and machine-learning analysis in VCNs.