Research

I explore applied Machine Learning for cybersecurity — focusing on threat detection and model robustness. Below are ideas and notes for future work and writing.

Areas of interest

• ML-based network intrusion detection and anomaly detection
• Adversarial attacks on ML models and defensive strategies
• Scalable algorithms for real-time threat detection

Planned experiments

1. Benchmark classical ML vs. deep learning on UNSW-NB15 & CSE-CIC IDS datasets
2. Test model robustness under adversarial perturbations
3. Design lightweight, interpretable models for deployment on resource-constrained devices