Microgrid Intrusion Detection System: A Machine Learning-Based Security Protection Solution for Energy Networks

Introduction to the Microgrid Intrusion Detection System

This project was released by Krishnagangwal on GitHub in May 2026 (link: https://github.com/Krishnagangwal/microgrid-IDS), an end-to-end microgrid intrusion detection framework based on IEEE research findings. Key content includes:

• Trained and tested using the UNSW-NB15 benchmark dataset
• Integrates four machine learning models: Decision Tree, Gradient Boosting, XGBoost, and CatBoost
• Introduces SHAP interpretability analysis to parse model decision logic
• Conducts real-time inference latency testing and multi-class attack identification
• Validates the statistical significance of model performance differences via the McNemar test

This system provides a complete technical reference for the security protection of critical energy infrastructure.

Background: Security Challenges of Microgrids

As the core of distributed energy systems, microgrids face cyber attack risks due to their highly interconnected nature (e.g., disrupting energy supply stability, causing power outages). Traditional IT security protection measures have adaptability issues:

• Microgrid communication protocols and architectures differ from traditional IT networks
• Extremely high real-time requirements; security detection must not affect system operation Therefore, targeted intrusion detection systems (IDS) have become a research focus.

Technical Architecture and Core Methods

Multi-Model Integration Strategy

• Decision Tree: Serves as the baseline model, providing interpretable classification rules
• Gradient Boosting: Trains weak learners serially, balancing accuracy and overfitting
• XGBoost: Regularization + parallel computing, suitable for large-scale security data
• CatBoost: Optimizes categorical feature processing, reducing manual feature engineering

Data Preprocessing and Cross-Validation

• Strict data separation to avoid leakage; process includes feature encoding, missing value handling, and standardization
• 5-fold stratified cross-validation to ensure the proportion of attack samples in each fold is consistent with the overall dataset

Performance Evaluation and Interpretability Evidence

SHAP Interpretability Analysis

• Bee Swarm Plot: Shows the direction and intensity of feature impacts
• Feature Importance Ranking: Quantifies feature contribution
• Force Plot: Visualizes the decision process for individual samples

Real-Time Performance and Attack Identification

• Model inference latency reaches millisecond level, meeting real-time requirements
• Supports fine-grained attack type identification (DoS, probing, etc.), reporting detection rates and false positive rates for each type

Statistical Testing

• Uses the McNemar test to validate the statistical significance of model performance differences

Practical Application Value and Insights

• Energy Industry Practitioners: Provides a reference for translating academic results into practical solutions; modular code facilitates customization and modification
• Machine Learning Researchers: Provides a benchmark implementation in the cybersecurity field; SHAP integration helps understand model behavior

Summary and Future Outlook

This project is an important application of AI in critical infrastructure security; its open-source implementation provides a reference for related research. Future directions:

• Integrate deep learning to improve detection accuracy
• Integrate federated learning to enable distributed security collaboration
• Develop adaptive mechanisms to address new attack variants