Email Armour: A Multi-Layered Email Defense Solution
K.T.A.U Lakmal, Lihini Perera, S.P.K Padmika, Suranjini Silva, Dinithi Pandithage, Deemantha Siriwardana · 2024
Phishing and spam emails are evolving threats in today's digital landscape, posing significant risks to small and medium-sized businesses (SMBs). Despite the growing demand for email security solutions, SMBs often face barriers such as high costs and complexity. To address this gap, this study introduces a cost-effective, multi-layered email security solution leveraging machine learning. The proposed system comprises four core components: (1) spam detection using natural language processing (NLP) to analyze email content, (2) phishing URL and QR code detection by identifying malicious patterns and attributes, (3) attachment security analysis to detect malware and harmful scripts, and (4) suspicious URL detection focusing on JavaScript obfuscation. The model was trained and validated on publicly available datasets, achieving an overall accuracy of 85% and demonstrating effectiveness in identifying diverse email threats. Designed as an email client, this tool empowers SMBs to secure communication with an accessible and robust defense against email-based attacks