Intermediate

ML-Based Intrusion Detection

Build effective intrusion detection systems using classical machine learning algorithms on network flow data.

Flow-Based Features

Network flow features are the foundation of ML-based IDS:

  • Flow duration, byte count, packet count - Basic flow characteristics
  • Inter-arrival times - Timing patterns between packets
  • Flag counts - TCP flags (SYN, FIN, RST, PSH) distribution
  • Port patterns - Source/destination port usage and entropy
  • Payload statistics - Min/max/mean/std of packet sizes

Algorithm Comparison

AlgorithmAccuracySpeedInterpretability
Random ForestHigh (95%+)FastFeature importance available
XGBoostVery High (97%+)FastSHAP values for explanation
SVMHighSlow on large dataLow (kernel methods)
Logistic RegressionModerateVery FastHigh (coefficients)
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Multi-class vs binary: Start with binary classification (normal vs. attack). Once that works well, expand to multi-class to identify specific attack types (DoS, probe, exploit, botnet). Multi-class enables targeted response actions.

Building the Pipeline

  1. Data collection: Gather labeled network flow data (use CICIDS2017 for learning)
  2. Preprocessing: Handle missing values, encode categoricals, normalize numerics
  3. Feature selection: Use correlation analysis and feature importance to reduce dimensionality
  4. Training: Train with cross-validation, handle class imbalance with SMOTE or class weights
  5. Evaluation: Focus on recall (catch attacks) balanced with precision (avoid false alarms)
Top performer: XGBoost with properly tuned hyperparameters consistently achieves 97%+ accuracy on standard IDS datasets. Use RandomizedSearchCV for efficient hyperparameter tuning and SHAP for model interpretability.

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