Multimodal Zone-Aware Graph-Based Transformer with Continual Learning and Bio-Inspired Optimization for Email Spam Detection

Cyberattacks via email remain a major menace to people, companies, and critical infrastructures, and effective spam and phishing detection is a social concern. Nevertheless, the current methods, such as NetSpam and SMART, tend to have issues with non-homogenous data streams, lack of contextual knowledge, poor generalization, and inability to adapt to changing attack patterns. The existing techniques are not strong in terms of multimodal fusion and cannot effectively transfer trust or update risk scores in dynamic conditions. To overcome these shortcomings, this paper presents a Zone-Aware Multimodal Graph-Based Transformer that combines text, image, video, and metadata streams in a smooth manner to detect threats in emails. The three key novelties of the proposed framework include AAGFusion to contrastively align multimodal features and hierarchically fuse them using transformers; MAGNN-SASO to classify zones, compute similarity across zones, and optimize bio-inspired optimization; and Q-BayesTrustNet-X to propagate trust, risk score, Bayesian calibration, and continual learning, and provide interpretable feedback by using LRP-based explainability. The experimental findings prove that the proposed system has a high level of performance, with the accuracy, precision, and specificity reaching 98.57, 97.51, and 99.48, respectively, which proves its efficiency in high-fidelity and real-world spam and phishing detection.