AI & Machine LearningSecurity Data Scientist • 2024

Network Intrusion Detection System (NIDS)

ML-driven binary network traffic classification for automated threat detection.

99.2% precision on malicious traffic

Automated feature engineering pipeline

Reproducible data science workflow

Low false-positive rate under test

System Architecture

End-to-end data pipeline from raw PCAP flow parsing through feature scaling, ensemble inference, and alert classification.

Key Architecture Components

Packet Flow Extraction: Ingests raw packet data and aggregates bidirectional session statistics.
Feature Transformation Pipeline: Standardizes numerical flow rates, packet lengths, and duration windows.
Ensemble Classification Model: Tuned tree-based estimators delivering instant binary attack prediction.
Security Telemetry Output: Outputs threat probability scores and flag metadata for SOC monitoring.

Data Pipeline FlowRaw Network Flows → Flow Aggregation → Feature Scaling → ML Ensemble Model → Threat Classification & Alerting

Engineering Challenges & Solutions

Challenge: High-dimensional raw network packets containing noisy, redundant, and uninformative header attributes.

Engineering Solution: Engineered mutual information gain selection and correlation matrix pruning to isolate top discriminative features, shrinking model training time by 55%.

Challenge: Severe class imbalance between ubiquitous benign traffic and sparse malicious intrusion packets.

Engineering Solution: Utilized SMOTE oversampling and hyperparameter-tuned ensemble classifiers (Random Forest & XGBoost) to achieve high sensitivity on stealth attacks.