Comparing intrusion detection in batch and streaming environments
Evaluated preprocessing and algorithm choices across batch and real-time IDS settings to guide production deployments.
Streaming accuracy
99.9%
Algorithms benchmarked
8
Preprocessing variants
3
Overview
Security teams needed clarity on which intrusion detection algorithms hold up under real-time constraints.
Our researchers conducted a side-by-side evaluation across batch and streaming environments using consistent datasets.
Challenges
- Model rankings shift dramatically depending on label granularity and preprocessing.
- Streaming deployments must handle concept drift while maintaining accuracy.
- Practitioners lacked a playbook for selecting algorithms under differing operational regimes.
Approach
Feature engineering scenarios
Created multiple preprocessing variants with different feature and label consolidations to test robustness.
Batch benchmarking
Evaluated SVM, MLP, decision trees, Naive Bayes, and k-NN in WEKA using 10-fold cross-validation.
Streaming evaluation
Ran Hoeffding Trees, IBLStream, Naive Bayes, and OzaBoost in MOA with prequential testing and fading factors to simulate drift.
Impact delivered
- Identified top-performing models for binary, five-class, and multi-class setups in batch mode.
- Showed that ensemble methods like OzaBoost maintain high accuracy and fast recovery under drift.
- Delivered actionable recommendations for aligning IDS model selection with deployment constraints.
Key lessons
- Always align preprocessing and labeling choices with intended deployment metrics.
- Streaming evaluations require drift-aware protocols to reveal true model resilience.
- One-size-fits-all algorithm recommendations rarely hold across operational contexts.
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