Building learning-focused security systems at the intersection of adversarial ML, Zero-Trust networks, and intrusion detection.

I’m a Computer Science student and aspiring cybersecurity enthusiast exploring how machine learning systems can be attacked, evaluated, and hardened.

Recently, I’ve been working on a network-level adversarial ML project focused on:

• ML-based intrusion detection using network traffic data
• Adversarial evasion attacks on tabular network features
• Zero-Trust policy enforcement with contextual trust scoring
• Security-focused evaluation using ROC, FPR, and FNR metrics

I’m continuously strengthening my fundamentals in cybersecurity, network security, and system-level defense while exploring how AI systems operate in adversarial environments.

• Adversarial attack simulation in ML-based intrusion detection systems
• Zero-Trust network policy enforcement models
• Building structured, security-focused ML experimentation frameworks
• Strengthening my network security and system hardening knowledge

• Security-focused ML robustness testing
• Intrusion detection & anomaly detection systems
• Zero-Trust architecture simulations
• Open-source cybersecurity projects

• Understanding real-world adversarial attack strategies
• Improving network-level detection modeling
• Advanced robustness evaluation techniques
• Practical cybersecurity implementation patterns

• Cybersecurity fundamentals
• Network security & system hardening
• Ethical hacking basics
• Adversarial machine learning concepts
• Cloud security fundamentals (AWS & secure deployment practices)