Hierarchical Polysemantic Feature Embedding for Autonomous Ransomware Detection

• URL: http://arxiv.org/abs/2502.06043v1
• Date: Sun, 09 Feb 2025 21:46:36 GMT
• Title: Hierarchical Polysemantic Feature Embedding for Autonomous Ransomware Detection
• Authors: Sergei Nikitka, Sebastian Harringford, Charlotte Montgomery, Algernon Braithwaite, Matthew Kowalski,
• Abstract summary: The evolution of ransomware requires the development of more sophisticated detection techniques. The proposed framework embeds ransomware-relevant features into a non-Euclidean space. Experimental evaluations demonstrated that the framework consistently outperformed conventional machine learning-based models. The proposed method maintains a balance between detection performance and processing overhead, making it a viable candidate for real-world cybersecurity applications.
• Score: 0.0
• License:
• Abstract: The evolution of ransomware requires the development of more sophisticated detection methodologies capable of identifying malicious behaviors beyond traditional signature-based and heuristic techniques. The proposed Hierarchical Polysemantic Feature Embedding framework introduces a structured approach to ransomware detection through hyperbolic feature representations that capture hierarchical dependencies within executable behaviors. By embedding ransomware-relevant features into a non-Euclidean space, the framework maintains a well-defined decision boundary, ensuring improved generalization across previously unseen ransomware variants. Experimental evaluations demonstrated that the framework consistently outperformed conventional machine learning-based models, achieving higher detection accuracy while maintaining low false positive rates. The structured clustering mechanism employed within the hyperbolic space enabled robust classification even in the presence of obfuscation techniques, delayed execution strategies, and polymorphic transformations. Comparative analysis highlighted the limitations of existing detection frameworks, particularly in their inability to dynamically adapt to evolving ransomware tactics. Computational efficiency assessments indicated that the proposed method maintained a balance between detection performance and processing overhead, making it a viable candidate for real-world cybersecurity applications. The ability to detect emerging ransomware families without requiring extensive retraining demonstrated the adaptability of hierarchical embeddings in security analytics.

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)
• Neural Encrypted State Transduction for Ransomware Classification: A Novel Approach Using Cryptographic Flow Residuals [0.0]
  An approach based on Neural Encrypted State Transduction (NEST) is introduced to analyze cryptographic flow residuals. NEST maps state transitions dynamically, enabling high-confidence classification without requiring direct access to decrypted execution traces.
  arXiv  Detail & Related papers  (2025-02-07T21:26:51Z)
• Intelligent Code Embedding Framework for High-Precision Ransomware Detection via Multimodal Execution Path Analysis [0.0]
  A novel framework was developed to identify ransomware activity through multimodal execution path analysis. It integrates high-dimensional embeddings and dynamic derivation mechanisms to capture behavioral patterns across diverse attack variants.
  arXiv  Detail & Related papers  (2025-01-27T07:51:51Z)
• 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)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy. We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods. By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• 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)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• 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)
• A general framework for defining and optimizing robustness [74.67016173858497]
  We propose a rigorous and flexible framework for defining different types of robustness properties for classifiers. Our concept is based on postulates that robustness of a classifier should be considered as a property that is independent of accuracy. We develop a very general robustness framework that is applicable to any type of classification model.
  arXiv  Detail & Related papers  (2020-06-19T13:24:20Z)
• Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition [133.35968094967626]
  Skeleton-based action recognition has attracted increasing attention due to its strong adaptability to dynamic circumstances. With the help of deep learning techniques, it has also witnessed substantial progress and currently achieved around 90% accuracy in benign environment. Research on the vulnerability of skeleton-based action recognition under different adversarial settings remains scant.
  arXiv  Detail & Related papers  (2020-05-14T17:12:52Z)

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.