projects

one-class intrusion detection for IoMT network traffic (thesis)

• Developed a CNN-DROCC one-class model for anomaly detection in Internet of Medical Things network traffic.
• Achieved 0.92 accuracy on CIC-IoMT-2024 and 0.97 on WUSTL-EHMS-2020.
• Converted tabular traffic features into structured 6×6 grayscale representations.
• Applied localized adversarial training to learn compact normal-data boundaries and prevent representation collapse.

• PyTorch
• CNN-DROCC
• Python
• CIC-IoMT-2024
• WUSTL-EHMS-2020

• github ↗

multimodal QR code phishing detection

• Engineered a three-branch neural network combining a Transformer lexical encoder with URL and QR structural features.
• Achieved 0.96 binary classification accuracy on an imbalanced phishing dataset.
• Designed cross-modal attention across 32-dimensional branch embeddings to learn interactions between modalities.

• PyTorch
• Transformer
• MLP
• Cross-Modal Attention

• github ↗

multi-user document RAG platform

• Built a multi-user document RAG application with secure PDF, DOCX, and URL ingestion; hybrid retrieval, reranking, and source-cited Gemini responses.
• Implemented JWT authentication, capsule-level data isolation, background ingestion workers, persistent chat history, automatic capsule summaries, and continuation handling for long LLM outputs.
• Deployed on Google Cloud with PostgreSQL, Redis, and Qdrant-backed services configured for production use.

• FastAPI
• PostgreSQL
• Redis
• Qdrant
• Gemini
• Google Cloud

• github ↗