Maritime Domain Awareness (MDA) is critical for global security and economic stability, yet it is increasingly challenged by sophisticated adversarial tactics such as signal spoofing and “dark vessel” activities. Traditional surveillance systems, often reliant on single-sensor modalities, are ill-equipped to handle these deceptive behaviors. To address this, we propose the Multimodal Attention-based Fusion Transformer (MAFT), a novel deep learning architecture that integrates four distinct data modalities—Aerial imagery, Synthetic Aperture Radar (SAR), acoustic signatures, and Automatic Identification System (AIS) data—to achieve robust and interpretable maritime anomaly detection. A key contribution of our work is a principled synthetic data generation pipeline that creates a large-scale, labeled dataset (16,000 samples) for four critical anomaly types: Correlated Activity, Dark Vessels, AIS Spoofing, and Kinematic Anomalies. MAFT architecture employs modality-specific encoders to project heterogeneous data into a common 320-dimensional embedding space. These embeddings are then tokenized and supplied to a multi-layer Transformer Encoder, which leverages a self-attention mechanism for late-fusion, learning complex, non-linear inter-modal relationships. We also introduce “modality dropout” (p = 0.3) as a regularization technique to enhance model robustness against sensor failure or data unavailability. Quantitative analysis shows our model achieves a 97.02% F1-score and a significantly improved Expected Calibration Error (ECE) of 0.011, outperforming Early Fusion CNN, Mid-Fusion MLP, and Decision-Ensemble baselines. Furthermore, computational profiling confirms an inference latency of 26.54 ms, demonstrating operational readiness for real-time deployment. Analysis of the model’s attention weights suggests that MAFT not only accurately classifies maritime activities but also provides a high degree of model interpretability, offering crucial, data-driven insights for maritime security operators.

Fuel station drive-offs, wherein the drivers simply drive off without paying, are a major issue in the UK (United Kingdom) due to rising fuel costs and financial hardships. The phenomenon has increased greatly over the last few years, with reports indicating a substantial increase in such events in the major cities. Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases. Such systems typically involve costly camera installation and maintenance and are consequently out of the budget of small fuel stations. These conventional approaches also fall short regarding real-time recognition, particularly regarding first-time impostors using fictitious plates, which represent an increasingly significant proportion of such forgery. This research presents an AI (Artificial Intelligence)-driven detection system using the MOT (Ministry of Transport) History API (Application Programming Interface) to scan in real-time at gas stations to recognize and prevent such fraud. The system integrates various state-of-the-art technologies to offer a foolproof system. Using the latest YOLO (You Only Look Once) model to recognize number plates and EasyOCR (Optical Character Recognition) to recognize characters, the system correctly reads license plates in various environmental conditions like lighting, viewpoint, and weather conditions. This approach minimizes the utilization of expensive camera systems and employs cheaper ANPR (Automatic Number Plate Recognition) gear, availing existing installed surveillance cameras on filling stations. The system operates with a basic web-based application to notify operators of stolen vehicles in real-time, enabling them to react immediately. Real-world testing achieves 84% success with CCTV (Closed-Circuit Television) images, depicting its real-world applicability. The results indicate that the AI-driven solution offers a monumental leap compared to current practices, giving fuel stations a cost-effective and efficient means of reducing financial loss from drive-off incidents.