Hierarchical Manifold Projection for Ransomware Detection: A Novel Geometric Approach to Identifying Malicious Encryption Patterns
Frederick Pembroke, Eleanor Featherstonehaugh, Sebastian Wetherington,, Harriet Fitzgerald, Maximilian Featherington, Peter Idliman

TL;DR
This paper introduces a hierarchical manifold projection method for ransomware detection that captures geometric patterns in encryption sequences, offering robustness against obfuscation and adaptability to diverse variants.
Contribution
It presents a novel geometric classification framework that transforms encryption workflows into manifold embeddings, enhancing detection robustness and scalability over traditional methods.
Findings
High detection accuracy across ransomware variants
Robustness against obfuscation and code modifications
Efficient real-time processing for large datasets
Abstract
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…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Chaos-based Image/Signal Encryption · Digital and Cyber Forensics
