Hierarchical Cryptographic Signature Mapping for Ransomware Classification: A Structural Decomposition Approach

• URL: http://arxiv.org/abs/2501.19120v1
• Date: Fri, 31 Jan 2025 13:23:51 GMT
• Title: Hierarchical Cryptographic Signature Mapping for Ransomware Classification: A Structural Decomposition Approach
• Authors: Dominic Abernethy, Nathaniel Weatherstone, Tristan Ravensdale, Lafedi Svet,
• Abstract summary: A hierarchical classification framework designed to analyze structural cryptographic properties provides a novel approach to distinguishing malicious encryption. The study examines how cryptographic feature mapping facilitates improved classification accuracy. The layered structural analysis further enhances forensic investigations, enabling security analysts to dissect encryption to trace attack origins.
• Score: 0.0
• License:
• Abstract: Encryption-based cyber threats continue to evolve, leveraging increasingly sophisticated cryptographic techniques to evade detection and persist within compromised systems. A hierarchical classification framework designed to analyze structural cryptographic properties provides a novel approach to distinguishing malicious encryption from legitimate cryptographic operations. By systematically decomposing encryption workflows into hierarchical layers, the classification method enhances the ability to recognize distinct patterns across diverse threat variants, reducing the dependence on predefined signatures that often fail against rapidly mutating threats. The study examines how cryptographic feature mapping facilitates improved classification accuracy, highlighting the role of entropy, key exchange mechanisms, and algorithmic dependencies in distinguishing harmful encryption activities. Through experimental validation, the framework demonstrated a high degree of precision across multiple attack families, outperforming conventional classification techniques while maintaining computational efficiency suitable for large-scale cybersecurity applications. The layered structural analysis further enhances forensic investigations, enabling security analysts to dissect encryption workflows to trace attack origins and identify commonalities across different campaigns. The methodology strengthens proactive threat mitigation efforts, offering a scalable and adaptable solution that accounts for both known and emerging encryption-based cyber threats. Comparative evaluations illustrate the advantages of structural decomposition in mitigating false positives and negatives, reinforcing the reliability of cryptographic signature classification in real-world security environments.

Related papers

• Hierarchical Entropy Disruption for Ransomware Detection: A Computationally-Driven Framework [0.0]
  Monitoring entropy variations offers an alternative approach to identifying unauthorized data modifications. A framework leveraging hierarchical entropy disruption was introduced to analyze deviations in entropy distributions. evaluating the framework across multiple ransomware variants demonstrated its capability to achieve high detection accuracy.
  arXiv  Detail & Related papers  (2025-02-12T23:29:06Z)
• Hierarchical Manifold Projection for Ransomware Detection: A Novel Geometric Approach to Identifying Malicious Encryption Patterns [0.0]
  Encryption-based cyber threats continue to evolve, employing increasingly sophisticated techniques to bypass traditional detection mechanisms. A novel classification framework structured through hierarchical manifold projection introduces a mathematical approach to detecting malicious encryption. The proposed methodology transforms encryption sequences into structured manifold embeddings, ensuring classification robustness through non-Euclidean feature separability.
  arXiv  Detail & Related papers  (2025-02-11T23:20:58Z)
• Hierarchical Entropic Diffusion for Ransomware Detection: A Probabilistic Approach to Behavioral Anomaly Isolation [0.0]
  This paper introduces a structured entropy-based anomaly classification mechanism. It tracks fluctuations in entropy evolution to differentiate between benign cryptographic processes and unauthorized encryption attempts. It maintains high classification accuracy across diverse ransomware families, outperforming traditional-based and signature-driven approaches.
  arXiv  Detail & Related papers  (2025-02-06T08:55:11Z)
• Semantic Entanglement-Based Ransomware Detection via Probabilistic Latent Encryption Mapping [0.0]
  Probabilistic Latent Encryption Mapping models encryption behaviors through statistical representations of entropy deviations and probabilistic dependencies in execution traces. Evaluations demonstrate that entropy-driven classification reduces false positive rates while maintaining high detection accuracy across diverse ransomware families and encryption methodologies. The ability to systematically infer encryption-induced deviations without requiring static attack signatures strengthens detection against adversarial evasion techniques.
  arXiv  Detail & Related papers  (2025-02-04T21:27:58Z)
• Hierarchical Pattern Decryption Methodology for Ransomware Detection Using Probabilistic Cryptographic Footprints [0.0]
  The framework combines advanced clustering algorithms with machine learning to isolate ransomware-induced anomalies. It effectively distinguishes malicious encryption operations from benign activities while maintaining low false positive rates. The inclusion of real-time anomaly evaluation ensures rapid response capabilities, addressing critical latency challenges in ransomware detection.
  arXiv  Detail & Related papers  (2025-01-25T05:26:17Z)
• Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
  We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem. These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem. We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
  arXiv  Detail & Related papers  (2025-01-25T04:53:36Z)
• Secure Semantic Communication With Homomorphic Encryption [52.5344514499035]
  This paper explores the feasibility of applying homomorphic encryption to SemCom. We propose a task-oriented SemCom scheme secured through homomorphic encryption.
  arXiv  Detail & Related papers  (2025-01-17T13:26:14Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation [57.92972327649165]
  This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
  arXiv  Detail & Related papers  (2021-09-07T21:24:36Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.