Explainable Vulnerability Detection in C/C++ Using Edge-Aware Graph Attention Networks

Detecting security vulnerabilities in source code remains challenging, particularly due to class imbalance in real-world datasets where vulnerable functions are under-represented. Existing learning-based methods often optimise for recall, leading to high false positive rates and reduced usability in development workflows. Furthermore, many approaches lack explainability, limiting their integration into security workflows. This paper presents ExplainVulD, a graph-based framework for vulnerability detection in C/C++ code. The method constructs Code Property Graphs and represents nodes using dual-channel embeddings that capture both semantic and structural information. These are processed by an edge-aware attention mechanism that incorporates edge-type embeddings to distinguish among program relations. To address class imbalance, the model is trained using class-weighted cross-entropy loss. ExplainVulD achieves a mean accuracy of 88.25 percent and an F1 score of 48.23 percent across 30 independent runs on the ReVeal dataset. These results represent relative improvements of 4.6 percent in accuracy and 16.9 percent in F1 score compared to the ReVeal model, a prior learning-based method. The framework also outperforms static analysis tools, with relative gains of 14.0 to 14.1 percent in accuracy and 132.2 to 201.2 percent in F1 score. Beyond improved detection performance, ExplainVulD produces explainable outputs by identifying the most influential code regions within each function, supporting transparency and trust in security triage.

Efficient UAVs Deployment and Resource Allocation in UAV-Relay Assisted Public Safety Networks for Video Transmission

Wireless communication highly depends on the cellular ground base station (GBS). A failure of the cellular GBS, fully or partially, during natural or man-made disasters creates a communication gap in the disaster-affected areas. In such situations, public safety communication (PSC) can significantly save the national infrastructure, property, and lives. Throughout emergencies, the PSC can provide mission-critical communication and video transmission services in the affected area. Unmanned aerial vehicles (UAVs) as flying base stations (UAV-BSs) are particularly suitable for PSC services as they are flexible, mobile, and easily deployable. This manuscript considers a multi-UAV-assisted PSC network with an observational UAV receiving videos from the affected area's ground users (AGUs) and transmitting them to the nearby GBS via a relay UAV. The objective of the proposed study is to maximize the average utility of the video streams generated by the AGUs upon reaching the GBS. This is achieved by optimizing the positions of the observational and relay UAVs, as well as the distribution of communication resources, such as bandwidth, and transmit power, while satisfying the system-designed constraints, such as transmission rate, rate outage probability, transmit power budget, and available bandwidth. To this end, a joint UAVs placement and resource allocation problem is mathematically formulated. The proposed problem poses a significant challenge for a solution. Considering the block coordinate descent and successive convex approximation techniques, an efficient iterative algorithm is proposed. Finally, simulation results are provided which show that our proposed approach outperforms the existing methods.