GPU-Accelerated Interpretable Generalization for Rapid Cyberattack Detection and Forensics
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908455903690752 |
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| author | Huang, Shu-Ting Chung, Wen-Cheng Pai, Hao-Ting |
| author_facet | Huang, Shu-Ting Chung, Wen-Cheng Pai, Hao-Ting |
| contents | The Interpretable Generalization (IG) mechanism recently published in IEEE Transactions on Information Forensics and Security delivers state-of-the-art, evidence-based intrusion detection by discovering coherent normal and attack patterns through exhaustive intersect-and-subset operations-yet its cubic-time complexity and large intermediate bitsets render full-scale datasets impractical on CPUs. We present IG-GPU, a PyTorch re-architecture that offloads all pairwise intersections and subset evaluations to commodity GPUs. Implemented on a single NVIDIA RTX 4070 Ti, in the 15k-record NSL-KDD dataset, IG-GPU shows a 116-fold speed-up over the multi-core CPU implementation of IG. In the full size of NSL-KDD (148k-record), given small training data (e.g., 10%-90% train-test split), IG-GPU runs in 18 minutes with Recall 0.957, Precision 0.973, and AUC 0.961, whereas IG required down-sampling to 15k-records to avoid memory exhaustion and obtained Recall 0.935, Precision 0.942, and AUC 0.940. The results confirm that IG-GPU is robust across scales and could provide millisecond-level per-flow inference once patterns are learned. IG-GPU thus bridges the gap between rigorous interpretability and real-time cyber-defense, offering a portable foundation for future work on hardware-aware scheduling, multi-GPU sharding, and dataset-specific sparsity optimizations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14222 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU-Accelerated Interpretable Generalization for Rapid Cyberattack Detection and Forensics Huang, Shu-Ting Chung, Wen-Cheng Pai, Hao-Ting Cryptography and Security The Interpretable Generalization (IG) mechanism recently published in IEEE Transactions on Information Forensics and Security delivers state-of-the-art, evidence-based intrusion detection by discovering coherent normal and attack patterns through exhaustive intersect-and-subset operations-yet its cubic-time complexity and large intermediate bitsets render full-scale datasets impractical on CPUs. We present IG-GPU, a PyTorch re-architecture that offloads all pairwise intersections and subset evaluations to commodity GPUs. Implemented on a single NVIDIA RTX 4070 Ti, in the 15k-record NSL-KDD dataset, IG-GPU shows a 116-fold speed-up over the multi-core CPU implementation of IG. In the full size of NSL-KDD (148k-record), given small training data (e.g., 10%-90% train-test split), IG-GPU runs in 18 minutes with Recall 0.957, Precision 0.973, and AUC 0.961, whereas IG required down-sampling to 15k-records to avoid memory exhaustion and obtained Recall 0.935, Precision 0.942, and AUC 0.940. The results confirm that IG-GPU is robust across scales and could provide millisecond-level per-flow inference once patterns are learned. IG-GPU thus bridges the gap between rigorous interpretability and real-time cyber-defense, offering a portable foundation for future work on hardware-aware scheduling, multi-GPU sharding, and dataset-specific sparsity optimizations. |
| title | GPU-Accelerated Interpretable Generalization for Rapid Cyberattack Detection and Forensics |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2507.14222 |