Intelligent Code Embedding Framework for High-Precision Ransomware Detection via Multimodal Execution Path Analysis

• URL: http://arxiv.org/abs/2501.15836v1
• Date: Mon, 27 Jan 2025 07:51:51 GMT
• Title: Intelligent Code Embedding Framework for High-Precision Ransomware Detection via Multimodal Execution Path Analysis
• Authors: Levi Gareth, Maximilian Fairbrother, Peregrine Blackwood, Lucasta Underhill, Benedict Ruthermore,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: Modern threat landscapes continue to evolve with increasing sophistication, challenging traditional detection methodologies and necessitating innovative solutions capable of addressing complex adversarial tactics. A novel framework was developed to identify ransomware activity through multimodal execution path analysis, integrating high-dimensional embeddings and dynamic heuristic derivation mechanisms to capture behavioral patterns across diverse attack variants. The approach demonstrated high adaptability, effectively mitigating obfuscation strategies and polymorphic characteristics often employed by ransomware families to evade detection. Comprehensive experimental evaluations revealed significant advancements in precision, recall, and accuracy metrics compared to baseline techniques, particularly under conditions of variable encryption speeds and obfuscated execution flows. The framework achieved scalable and computationally efficient performance, ensuring robust applicability across a range of system configurations, from resource-constrained environments to high-performance infrastructures. Notable findings included reduced false positive rates and enhanced detection latency, even for ransomware families employing sophisticated encryption mechanisms. The modular design allowed seamless integration of additional modalities, enabling extensibility and future-proofing against emerging threat vectors. Quantitative analyses further highlighted the system's energy efficiency, emphasizing its practicality for deployment in environments with stringent operational constraints. The results underline the importance of integrating advanced computational techniques and dynamic adaptability to safeguard digital ecosystems from increasingly complex threats.

Related papers

• Decentralized Entropy-Based Ransomware Detection Using Autonomous Feature Resonance [0.0]
  A novel approach, termed Autonomous Feature Resonance, is introduced to address the limitations of traditional ransomware detection methods. The proposed method achieves an overall detection accuracy of 97.3%, with false positive and false negative rates of 1.8% and 2.1%, respectively.
  arXiv  Detail & Related papers  (2025-02-14T00:26:10Z)
• Hierarchical Polysemantic Feature Embedding for Autonomous Ransomware Detection [0.0]
  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.
  arXiv  Detail & Related papers  (2025-02-09T21:46:36Z)
• Algorithmic Segmentation and Behavioral Profiling for Ransomware Detection Using Temporal-Correlation Graphs [0.0]
  A novel framework was introduced, leveraging Temporal-Correlation Graphs to model the intricate relationships and temporal patterns inherent in malicious operations. Experiments demonstrated the framework's effectiveness across diverse ransomware families, with consistently high precision, recall, and overall detection accuracy. The research contributes to advancing cybersecurity technologies by integrating dynamic graph analytics and machine learning for future innovations in threat detection.
  arXiv  Detail & Related papers  (2025-01-29T06:09:25Z)
• 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)
• Unveiling Zero-Space Detection: A Novel Framework for Autonomous Ransomware Identification in High-Velocity Environments [0.0]
  The proposed Zero-Space Detection framework identifies latent behavioral patterns through unsupervised clustering and advanced deep learning techniques. It operates effectively in high-velocity environments by integrating multi-phase filtering and ensemble learning for refined decision-making. Experimental evaluation reveals high detection rates across diverse ransomware families, including LockBit, Conti, REvil, and BlackMatter.
  arXiv  Detail & Related papers  (2025-01-22T11:41:44Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System [52.93806509364342]
  This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. We introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features. We incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency.
  arXiv  Detail & Related papers  (2023-12-18T12:37:35Z)
• 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 Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning [122.49765136434353]
  We present an effective method, called Hamiltonian Monte Carlo with Accumulated Momentum (HMCAM), aiming to generate a sequence of adversarial examples. We also propose a new generative method called Contrastive Adversarial Training (CAT), which approaches equilibrium distribution of adversarial examples. Both quantitative and qualitative analysis on several natural image datasets and practical systems have confirmed the superiority of the proposed algorithm.
  arXiv  Detail & Related papers  (2020-10-15T16:07:26Z)

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.