Enterprise AI Analysis
LNNIDS: A Hybrid Liquid Neural Network based IDS for Known and Unknown IoT Attacks
This article introduces LNNIDS, a novel IDS method integrating spike encoding with a Hybrid Liquid Neural Network (HLLN) and DYNGraph for scalable attack classification in IoT networks.
Executive Impact
LNNIDS significantly advances IoT security by offering superior accuracy and adaptability compared to traditional and state-of-the-art methods, even with limited training data.
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Average Accuracy
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Training Data Used
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Better Accuracy (Known Attacks)
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Better Accuracy (Unknown Attacks)
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LNNIDS integrates spike encoding, a Hybrid Liquid Neural Network (HLLN), and a dynamic graph-based method (DYNGraph) to achieve robust and adaptive intrusion detection.
LNNIDS Core Process Flow
Flow Reconstruction
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Feature Extraction
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Feature Selection
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Feature Encoding
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Hybrid Liquid Layer (HLL)
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Attention Readout Layer
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DYNGraph Classification
The LNNIDS process starts by reconstructing network flows, extracting and selecting critical features, encoding them into spike states, processing them through a Hybrid Liquid Layer, refining with attention, and finally classifying using DYNGraph for dynamic pattern matching.
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Average classification time per flow
Spike encoding, combined with the dynamic weight assignment in the HLL architecture, enables LNNIDS to maintain very low classification times per flow, crucial for real-time anomaly detection in industrial IoT networks.
| Feature | Traditional Methods (ML/DL) | State-of-the-Art (STA) | LNNIDS |
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| Unknown Attack Detection |
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| Training Data Requirements |
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| Adaptability to Evolving Threats |
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| Computational Cost |
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LNNIDS significantly outperforms traditional ML/DL and even several state-of-the-art methods in detecting both known and unknown IoT attacks, requiring less training data and demonstrating superior adaptability and efficiency.
LNNIDS demonstrates high accuracy and robust performance across diverse IoT datasets, even with varying feature sets and training data ratios.
99.50%
Accuracy on Public Dataset-1 (Known Attacks)
LNNIDS achieved a remarkable 99.50% accuracy on known IoT attacks in Public Dataset-1, showcasing its effectiveness.
99.04%
Accuracy on Public Dataset-1 (Unknown Attacks)
Crucially, LNNIDS also achieved 99.04% accuracy on previously unseen (unknown) IoT attacks, demonstrating strong generalization.
Robustness with Limited Data
Scenario: Evaluation on Public Dataset-1 and Dataset-2 with varying training-to-testing ratios.
Challenge: Traditional IDSs struggle with low-data regimes and need extensive training.
Solution: LNNIDS maintains robust performance even with only 20% training data, achieving high accuracy by effectively capturing temporal dependencies and dynamic patterns through its HLLN and DYNGraph.
Result: Accuracy of 88.59% on Public Dataset-1 and 81.24% on Public Dataset-2 with just 5% training data, highlighting its viability in low-data environments where full training sets are not available.
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