Neural Encrypted State Transduction for Ransomware Classification: A Novel Approach Using Cryptographic Flow Residuals

• URL: http://arxiv.org/abs/2502.05341v1
• Date: Fri, 07 Feb 2025 21:26:51 GMT
• Title: Neural Encrypted State Transduction for Ransomware Classification: A Novel Approach Using Cryptographic Flow Residuals
• Authors: Barnaby Fortescue, Edmund Hawksmoor, Alistair Wetherington, Frederick Marlowe, Kevin Pekepok,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: Encrypted behavioral patterns provide a unique avenue for classifying complex digital threats without reliance on explicit feature extraction, enabling detection frameworks to remain effective even when conventional static and behavioral methodologies fail. A novel approach based on Neural Encrypted State Transduction (NEST) is introduced to analyze cryptographic flow residuals and classify threats through their encrypted state transitions, mitigating evasion tactics employed through polymorphic and obfuscated attack strategies. The mathematical formulation of NEST leverages transduction principles to map state transitions dynamically, enabling high-confidence classification without requiring direct access to decrypted execution traces. Experimental evaluations demonstrate that the proposed framework achieves improved detection accuracy across multiple ransomware families while exhibiting resilience against adversarial perturbations and previously unseen attack variants. The model maintains competitive processing efficiency, offering a practical balance between classification performance and computational resource constraints, making it suitable for large-scale security deployments. Comparative assessments reveal that NEST consistently outperforms baseline classification models, particularly in detecting ransomware samples employing delayed encryption, entropy-based obfuscation, and memory-resident execution techniques. The capacity to generalize across diverse execution environments reinforces the applicability of encrypted transduction methodologies in adversarial classification tasks beyond conventional malware detection pipelines. The integration of residual learning mechanisms within the transduction layers further enhances classification robustness, minimizing both false positives and misclassification rates across varied operational contexts.

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)
• Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly [79.07074710460012]
  adversarial vulnerability of deep neural networks (DNNs) has drawn great attention. An increasing number of transfer-based methods have been developed to fool black-box DNN models. We establish a transfer-based attack benchmark (TA-Bench) which implements 30+ methods.
  arXiv  Detail & Related papers  (2023-11-02T15:35:58Z)
• 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)
• Defensive Tensorization [113.96183766922393]
  We propose tensor defensiveization, an adversarial defence technique that leverages a latent high-order factorization of the network. We empirically demonstrate the effectiveness of our approach on standard image classification benchmarks. We validate the versatility of our approach across domains and low-precision architectures by considering an audio task and binary networks.
  arXiv  Detail & Related papers  (2021-10-26T17:00:16Z)
• 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)
• Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation [74.05906222376608]
  We propose adversarial self-supervision UDA (or ASSUDA) that maximizes the agreement between clean images and their adversarial examples by a contrastive loss in the output space. This paper is rooted in two observations: (i) the robustness of UDA methods in semantic segmentation remains unexplored, which pose a security concern in this field; and (ii) although commonly used self-supervision (e.g., rotation and jigsaw) benefits image tasks such as classification and recognition, they fail to provide the critical supervision signals that could learn discriminative representation for segmentation tasks.
  arXiv  Detail & Related papers  (2021-05-23T01:50:44Z)
• 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)

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.