TriMNet-P: Tri-Modality Network for Phishing Attack Detection on Social and Digital Communication Platforms
Suneeta Satpathy, Pratik Kumar Swain, Syed Khasim · 2025
Phishing attacks are increasingly spreading through social media channels using malicious URLs, spoofed websites, and malicious attachments that capture personal information. Conventional detection systems are at a loss when detecting such multimodal attacks because they are based on single-modality analysis. To overcome detection of such attacks, the proposed TriMNet-P (Tri-Modality Network for Phishing Attack Detection) uses deep learning to combine detection of textual, visual, and network behavioural features. A Context-Aware Modality Attention (CAMA) mechanism dynamically weights relevant inputs across email, WhatsApp, and Instagram platforms. Tested on Phishing Email, Open Phish, and CIC-IDS2017 datasets, TriMNet-P has attained 98.6% accuracy, 97.9% precision, 97.5% recall, and runs at only 6ms latency.