Machine Learning Solution for Power Grid Cybersecurity with GraphWavelets
- 1. Power Grid Injection Cyberattack Detection Using Graph Wavelets Table of Content: (By Sione Palu) • Power Systems Cybersecurity • False DIA (Data Injection Attacks) & Dummy DIA (DDIA) • Shortcomings of Current FDIA and DDIA Detection Methods • Enhancing Attack Detection: Overcoming Current Limitations • Machine Learning Solution for Power Grid Cybersecurity: ATSTGWCN • Recap & Final Thoughts
- 2. Power Systems Cybersecurity & Importance • Grid Stability & Reliability: Prevents blackouts and ensures consistent power. • Public Safety: Protects essential services and prevents harm. • Economic Protection: Avoids massive financial losses from disruptions. • Critical Infrastructure Defense: Safeguards foundational systems for all services. • National Security: Prevents attacks that could cripple national defense.
- 3. False DIA (Data Injection Attacks) & Dummy DIA (DDIA) False Data Injection Attacks (FDIA): • Cyberattacks where hackers inject false static data into power grid systems to manipulate sensor readings and control systems. • Alters state estimation by feeding false voltage/current values to grid operators, causing incorrect decisions (e.g., misrouting power). Dummy Data Injection Attacks (DDIA): • An advanced FDIA variant that uses "dummy" dynamic data, a fabricated measurements data closely resembles the normal. measurement data to evade detection. • Remain undetected longer to enable persistent attacks like gradual grid destabilization.
- 4. Shortcomings of Current FDIA and DDIA Detection Methods • DDIA attacks are hidden within normal data, rendering conventional clustering and distance-based detection methods ineffective. • Sophisticated FDIAs align with physical laws, mimicking legitimate data to avoid standard anomaly detection. • DDIA localization models lack scalability and built-in intelligent automation due to minimal research. • Current DDIA localization models remain untested for robustness and adaptability. • Many FDIA methods ignore grid topology’s non-Euclidean correlations, causing inaccuracies in attack localization.
- 5. Enhancing Attack Detection: Overcoming Current Limitations • Build DDIA models that reflect real scenarios, including AC estimation and incomplete topology, while capturing stealthy attack behaviors. • Combine spatio-temporal attention with graph convolution (e.g., wavelet GNNs) to detect DDIA by modeling both topology and time-based patterns. • Use graph wavelet networks to enhance feature extraction efficiency and accuracy through flexible frequency domain analysis. • Create robust multi-tag DDIA localizers (e.g., sandwich structures) that handle noise, topology changes, and scale variations. • Improve detection fidelity by incorporating complete AC models rather than relying on DC approximations.
- 6. Machine Learning Solution for Power Grid Cybersecurity: ATSTGWCN ATST-GWCN (Attention Spatio Temporal - Graph Wavelet Convolutional Neural Network): • Construct spatio-temporal graph inputs, accounting for incomplete topology from power grid time-series measurements. • Generate spatial and temporal attention matrices to capture critical structural and temporal relationships in grid data. • Utilize causal 1D convolution for temporal trend and attack signature extraction. • GWCN extracts non-Euclidean spatial features, capturing grid topology patterns. • Fuse spatio-temporal features from attention, temporal, and wavelet convolutions into a localization head to predict DDIA presence and compromised nodes. • Train the ATSTGWCN with labeled data. During inference, it provides both DDIA detection and precise localization, even with topological uncertainties.
- 7. Recap & Final Thoughts • ATSTGWCN Superior Detection Accuracy: It achieves ~98% FDIA/DDIA detection vs. 85–92% for baselines (e.g., SVM, GCN). • Robust to Incomplete Data: Maintains >95% accuracy even with 30% missing grid topology data, outperforming traditional methods that fail belo 80%. • Lower False Positives: Reduces false alarms by 40% compared to wavelet-only or CNN-only approaches. • Adaptive to Dynamic Attacks: Detects sophisticated DDIAs with 93% precision (baselines: <70%). • Computational Efficiency: Processes grid data 20% faster than hybrid GNN-RNN models. • Key Takeaway: ATSTGWCN sets a new standard for real-world power grid cybersecurity, balancing accuracy, adaptability, and speed.