Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911497779675136 |
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| author | Chhetri, Rajeeb Thapa Thapa, Saurab Kumar, Avinash Chen, Zhixiong |
| author_facet | Chhetri, Rajeeb Thapa Thapa, Saurab Kumar, Avinash Chen, Zhixiong |
| contents | A critical vulnerability of supervised deep learning in high-dimensional tabular domains is "generalization collapse": models form precise decision boundaries around known training distributions but fail catastrophically when encountering Out-of-Distribution (OOD) data. To overcome this, we propose Latent Sculpting, a hierarchical, two-stage representation learning architecture designed to enforce explicit structural boundaries prior to density estimation. In the first stage, a Transformer-based tabular encoder is trained using our novel Binary Latent Sculpting loss. This objective explicitly condenses benign network traffic into a dense, low-entropy hypersphere while enforcing a strict geometric minimum-distance margin for anomalous patterns. In the second stage, a Masked Autoregressive Flow (MAF) maps this structurally optimized manifold to calculate exact, probabilistic anomaly thresholds. We evaluate this methodology on the CIC-IDS-2017 benchmark under a rigorous zero-shot protocol, deliberately withholding complex attack classes during training to test true OOD generalization. Averaged across three random initialization seeds to ensure statistical robustness, our framework maintains near-perfect classification on known signatures (F1 = 0.980 +/- 0.000) while achieving an overall zero-shot OOD F1-Score of 0.867 +/- 0.021 and an AUROC of 0.913 +/- 0.010 at an 85th-percentile threshold. Most notably, the model achieves an average recall of 78.7% (peaking at 97.2%) on stealthy "Infiltration" attacks and over 94% on low-volume DoS variations - complex distributional shifts where standard supervised and unsupervised baselines historically suffer near-total detection failure. These empirical results demonstrate that explicitly decoupling topological manifold structuring from probabilistic density estimation establishes a highly stable and scalable defense against zero-day cyber threats. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_22179 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Chhetri, Rajeeb Thapa Thapa, Saurab Kumar, Avinash Chen, Zhixiong Machine Learning Cryptography and Security 68T07, 62H30 I.2.6; C.2.0 A critical vulnerability of supervised deep learning in high-dimensional tabular domains is "generalization collapse": models form precise decision boundaries around known training distributions but fail catastrophically when encountering Out-of-Distribution (OOD) data. To overcome this, we propose Latent Sculpting, a hierarchical, two-stage representation learning architecture designed to enforce explicit structural boundaries prior to density estimation. In the first stage, a Transformer-based tabular encoder is trained using our novel Binary Latent Sculpting loss. This objective explicitly condenses benign network traffic into a dense, low-entropy hypersphere while enforcing a strict geometric minimum-distance margin for anomalous patterns. In the second stage, a Masked Autoregressive Flow (MAF) maps this structurally optimized manifold to calculate exact, probabilistic anomaly thresholds. We evaluate this methodology on the CIC-IDS-2017 benchmark under a rigorous zero-shot protocol, deliberately withholding complex attack classes during training to test true OOD generalization. Averaged across three random initialization seeds to ensure statistical robustness, our framework maintains near-perfect classification on known signatures (F1 = 0.980 +/- 0.000) while achieving an overall zero-shot OOD F1-Score of 0.867 +/- 0.021 and an AUROC of 0.913 +/- 0.010 at an 85th-percentile threshold. Most notably, the model achieves an average recall of 78.7% (peaking at 97.2%) on stealthy "Infiltration" attacks and over 94% on low-volume DoS variations - complex distributional shifts where standard supervised and unsupervised baselines historically suffer near-total detection failure. These empirical results demonstrate that explicitly decoupling topological manifold structuring from probabilistic density estimation establishes a highly stable and scalable defense against zero-day cyber threats. |
| title | Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection |
| topic | Machine Learning Cryptography and Security 68T07, 62H30 I.2.6; C.2.0 |
| url | https://arxiv.org/abs/2512.22179 |