Enterprise AI Analysis
Intrusion detection in smart grids using artificial intelligence-based ensemble modelling
This paper proposes a novel Fog-based Artificial Intelligence (AI) framework for Smart Grid (SG) Networks. It utilizes Machine Learning (ML) and Deep Learning (DL)-based ensemble models to enhance the accuracy of detecting intrusions in SG networks. This work addresses class imbalance in network intrusion detection datasets and builds interpretable models for targeted security interventions. It is achieved by using ensemble modeling, such as Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN) for ML-based ensemble, while the DL ensembles consist of aggregated neural network models trained using TensorFlow. The study utilizes a large dataset custom-designed for SG intrusion detection, including CIC-IDS-Collection and a specifically designed Power System Intrusion dataset. Results demonstrate superior accuracy, precision, recall, and F1 Scores for the proposed ensemble models compared to single ML techniques, showcasing the effectiveness of ensemble modeling in improving intrusion detection in SGs.
Key Executive Impact Metrics
Our AI framework significantly boosts intrusion detection capabilities in Smart Grids, ensuring robust security and operational integrity.
0%
Accuracy
0%
Precision
0%
Recall
0%
F1-Score
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology Overview
The paper outlines a novel Fog-based Artificial Intelligence (AI) framework for Smart Grid (SG) Networks, utilizing Machine Learning (ML) and Deep Learning (DL)-based ensemble models. The framework aims to enhance intrusion detection accuracy and robustness in SG networks by addressing class imbalance and building interpretable models. Ensemble models include Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN) for ML, and aggregated neural network models for DL.
Details: The proposed approach involves a layered architecture where fog nodes perform localized, low-latency detection, and a central control center handles global analysis and complex threat mitigation. The system is designed for real-time data analysis from smart meters, IoT devices, and sensors. Preprocessing steps include data cleaning, normalization (StandardScaler, MinMaxScaler, RobustScaler), encoding (LabelEncoder), balancing (SMOTE), and feature extraction (PCA). The model is trained and evaluated on CIC-IDS-Collection and Power System Intrusion datasets.
Ensemble Models
Two types of ensemble models are utilized: ML-based and DL-based. Ensemble 1 combines LR, RF, and KNN. Ensemble 2 comprises Support Vector Machine (SVM), Gradient Boosting (GB), and Naive Bayes (NB). DL ensembles integrate predictions from three distinct neural network architectures.
Details: The DL models feature multi-layer perceptrons with dense layers, ReLU activations, and dropout layers for regularization. Model 1 has dense layers with 256, 128, 64, 32, and 8 neurons. Model 2 has dense layers with 256, 64, and 16 neurons. Model 3 has dense layers with 128, 256, and 128 neurons. Soft voting mechanisms are used to combine predicted probabilities from individual models to enhance robustness and accuracy.
Attack Analysis
The paper provides a mathematical formulation for major intrusion attacks in SG environments, including Denial of Service (DoS) and Man-in-the-Middle (MitM) attacks. It quantifies their impact on network functionality and demonstrates how the proposed ensemble model enhances detection accuracy against these threats.
Details: A DoS attack targets a specific node, making it unavailable, with its impact on network functionality proportional to the node's criticality. The resilience R of the Smart Grid under multiple DoS attacks is R = 1 - ΣC(ni)⋅ADOS(ni). A MitM attack intercepts communication between two nodes, with its impact proportional to the communication link's importance I = L(ni, nj)⋅AMitm(ni, nj). The ensemble model's ability to combine multiple DL architectures improves its capacity to detect and mitigate varied attack types.
Performance Results
The proposed ensemble models achieved superior performance compared to state-of-the-art methods. Ensemble 2 recorded the highest accuracy of 98.84% on the CIC-IDS-Collection dataset, outperforming previous CNN and LSTM models. Precision, recall, and F1-scores also demonstrated significant improvements.
Details: For the CIC-IDS Collection dataset, Ensemble 1 achieved 98.57% accuracy, 98.75% precision, 99.00% recall, and 98.25% F1-score. Ensemble 2 achieved 98.84% accuracy, 99.00% precision, 99.00% recall, and 99.00% F1-score. For the Power System dataset, Ensemble 1 achieved 98.75% accuracy, 99.05% precision, 99.20% recall, and 99.10% F1-score, while Ensemble 2 achieved 99.05% accuracy, 99.30% precision, 99.25% recall, and 99.27% F1-score. These results highlight the ensemble's robust capability in identifying diverse attacks and minimizing false positives.
0%
Achieved F1-Score on CIC-IDS Collection
Smart Grid Intrusion Detection Flow
Data Collection (SG Sensors/IoT)
→
Preprocessing (Fog Layer)
→
Localized Intrusion Detection (ML/DL Ensembles)
→
Aggregated Results to Control Center
→
Global Threat Analysis & Mitigation
→
Secure Smart Grid Network
| Feature | Ensemble Models | Traditional ML/DL |
|---|---|---|
| Accuracy |
|
|
| Robustness |
|
|
| Interpretability |
|
|
| Scalability |
|
|
Real-world Impact: Preventing Smart Grid Cyberattacks
The 2015 cyberattack on Ukraine's power grid, which led to widespread outages, underscores the critical need for robust intrusion detection. Our proposed AI-based ensemble model directly addresses such vulnerabilities by providing a multi-layered defense mechanism. By deploying localized detection at fog nodes and advanced threat analysis at a central control center, the system can detect and mitigate complex attacks like DoS and MitM in real-time. For instance, in a simulated scenario replicating the Ukraine attack, our model demonstrated the ability to detect and isolate compromised grid sections with 0%, preventing potential large-scale power disruptions and ensuring critical infrastructure resilience. This proactive approach significantly reduces the risk of operational shutdowns and safeguards energy distribution.
$0Detection F1-Score (simulated)
SignificantOutages Prevented (Estimated)
AI ROI Calculator
Estimate your potential savings and efficiency gains with an AI-powered intrusion detection system.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Implementation Roadmap
A phased approach to integrating AI into your Smart Grid security infrastructure.
Phase 1
Initial Setup & Data Integration (Weeks 1-4): Establish fog nodes, integrate SG sensors and IoT devices, and collect baseline network traffic data.
Phase 2
Model Training & Optimization (Weeks 5-12): Preprocess datasets, train ML and DL ensemble models on historical and real-time data, and fine-tune hyperparameters.
Phase 3
Deployment & Real-time Monitoring (Weeks 13-20): Deploy models to fog nodes and control center, activate real-time intrusion detection, and establish alert mechanisms.
Phase 4
Continuous Learning & Adaptation (Ongoing): Implement adaptive learning for continuous model refinement, regularly update detection algorithms, and conduct threat intelligence integration.
Ready to Transform Your Smart Grid Security?
Book a personalized consultation to explore how our AI-driven intrusion detection solutions can safeguard your critical infrastructure.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat