Conceived and trained an intrusion detection system (NIDS) utilizing supervised machine learning algorithms to identify hostile attack signatures and anomalous packet distributions.
Processed high-dimensional network flow datasets, performing feature extraction, packet header statistical modeling, and dimensionality reduction.
Trained ensemble classifiers (Random Forest, XGBoost) and deep neural architectures, benchmarked against precision, recall, and false-positive minimization.
Validated real-time detection latencies against live simulated traffic, demonstrating rapid alert dispatching for DDoS, port scanning, and brute-force intrusion vectors.
