Hierarchical Manifold Projection for Ransomware Detection: A Novel Geometric Approach to Identifying Malicious Encryption Patterns
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912292191338496 |
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| author | Pembroke, Frederick Featherstonehaugh, Eleanor Wetherington, Sebastian Fitzgerald, Harriet Featherington, Maximilian Idliman, Peter |
| author_facet | Pembroke, Frederick Featherstonehaugh, Eleanor Wetherington, Sebastian Fitzgerald, Harriet Featherington, Maximilian Idliman, Peter |
| contents | Encryption-based cyber threats continue to evolve, employing increasingly sophisticated techniques to bypass traditional detection mechanisms. Many existing classification strategies depend on static rule sets, signature-based matching, or machine learning models that require extensive labeled datasets, making them ineffective against emerging ransomware families that exhibit polymorphic and adversarial behaviors. A novel classification framework structured through hierarchical manifold projection introduces a mathematical approach to detecting malicious encryption workflows, preserving geometric consistencies that differentiate ransomware-induced modifications from benign cryptographic operations. The proposed methodology transforms encryption sequences into structured manifold embeddings, ensuring classification robustness through non-Euclidean feature separability rather than reliance on static indicators. Generalization capabilities remain stable across diverse ransomware variants, as hierarchical decomposition techniques capture multi-scale encryption characteristics while maintaining resilience against code obfuscation and execution flow modifications. Empirical analysis demonstrates that detection accuracy remains high even when encryption key variability, delayed execution tactics, or API call obfuscation strategies are introduced, reinforcing the reliability of manifold-based classification. Real-time scalability assessments confirm that the proposed approach maintains computational efficiency across increasing dataset volumes, validating its applicability to large-scale threat detection scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Manifold Projection for Ransomware Detection: A Novel Geometric Approach to Identifying Malicious Encryption Patterns Pembroke, Frederick Featherstonehaugh, Eleanor Wetherington, Sebastian Fitzgerald, Harriet Featherington, Maximilian Idliman, Peter Cryptography and Security Encryption-based cyber threats continue to evolve, employing increasingly sophisticated techniques to bypass traditional detection mechanisms. Many existing classification strategies depend on static rule sets, signature-based matching, or machine learning models that require extensive labeled datasets, making them ineffective against emerging ransomware families that exhibit polymorphic and adversarial behaviors. A novel classification framework structured through hierarchical manifold projection introduces a mathematical approach to detecting malicious encryption workflows, preserving geometric consistencies that differentiate ransomware-induced modifications from benign cryptographic operations. The proposed methodology transforms encryption sequences into structured manifold embeddings, ensuring classification robustness through non-Euclidean feature separability rather than reliance on static indicators. Generalization capabilities remain stable across diverse ransomware variants, as hierarchical decomposition techniques capture multi-scale encryption characteristics while maintaining resilience against code obfuscation and execution flow modifications. Empirical analysis demonstrates that detection accuracy remains high even when encryption key variability, delayed execution tactics, or API call obfuscation strategies are introduced, reinforcing the reliability of manifold-based classification. Real-time scalability assessments confirm that the proposed approach maintains computational efficiency across increasing dataset volumes, validating its applicability to large-scale threat detection scenarios. |
| title | Hierarchical Manifold Projection for Ransomware Detection: A Novel Geometric Approach to Identifying Malicious Encryption Patterns |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2502.08013 |