About This Architecture

Hybrid IDS combining Random Forest baseline with PPO reinforcement learning detects network attacks on CICIDS-2017 data with 78 features across 692,703 flows. Preprocessed traffic feeds both a stable 100-tree Random Forest classifier and an adaptive PPO agent that learns optimal detection policies, with ADWIN drift detection triggering PPO retraining when performance degrades. The evaluation layer compares F1 scores per temporal chunk and selects the best detector, balancing production stability with adaptive learning. This architecture solves the cold-start and concept-drift problems in network security by combining supervised baseline reliability with reinforcement learning agility. Fork and customize this diagram on Diagrams.so to design your own hybrid detection pipeline or adapt it for different datasets and drift detection thresholds.

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How can I build an intrusion detection system that adapts to concept drift while maintaining production stability?

This diagram shows a hybrid IDS that pairs a stable Random Forest baseline (100 decision trees, class-weighted for imbalance) with a PPO reinforcement learning agent trained on 78-dimensional network features. ADWIN drift detection monitors performance per temporal chunk and triggers PPO retraining when significant degradation occurs, while the evaluation layer selects the best detector by F1 scor