I'm Aarnav Singh — a final-year B.Tech CSE (Cybersecurity) student researching how Zero-Trust contextual policy can absorb adversarial machine-learning attacks, and building the security tooling around that research.
I got into security through an internship at HCLTech, working across enterprise network security, Microsoft Azure infrastructure, and DevSecOps automation in Python. That raised a question that's stuck with me: as detection leans harder on machine learning, what happens when an attacker learns to fool the model itself?
My major project answers that for Zero-Trust network architectures. I built a six-layer system pairing an ML intrusion-detection classifier with a context-aware policy engine, then attacked it with domain-constrained FGSM and PGD methods — and measured how much Zero-Trust context could compensate, with no model retraining at all.
Alongside the research I built an applied counterpart — a full-stack Hybrid Behavioral Intrusion Detection System with a real-time dashboard, closer to a production SOC tool.
A six-layer Python/PyTorch system showing that Zero-Trust contextual policy can absorb the damage of successful FGSM/PGD evasion against an ML intrusion-detection classifier — without retraining.
A six-layer Zero-Trust security system combining an ML intrusion-detection classifier with a context-aware policy engine, stress-tested against domain-constrained adversarial attacks.
A full-stack IDS combining rule-based detection with Isolation-Forest anomaly detection, paired with a real-time monitoring dashboard.