Examining Windows File System IRP Operations with Machine Learning for Ransomware Detection

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 8, 2024

Abstract This study introduces an innovative approach to ransomware detection on Windows operating systems by leveraging Generative Adversarial Networks (GANs) analyze file system I/O Request Packet (IRP) operations. The proposed method demonstrates a significant improvement in identifying activities through the dynamic monitoring of IRP operations, distinguishing between benign and malicious behaviors with high accuracy. research highlights application GANs as powerful tool cybersecurity, capable adapting evolving tactics without need for predefined threat signatures. Through rigorous testing, model showcased notable advancements over traditional methods, indicating its potential enhance real-world cybersecurity defenses. findings suggest shift towards more adaptive, machine learning-based solutions combating increasing complexity cyber threats.

Language: Английский

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Federated Learning-Based Ransomware Detection via Indicators of Compromise

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 18, 2024

Ransomware attacks have become increasingly prevalent and sophisticated, posing significant threats to data security organizational operations worldwide. Leveraging a federated learning-based approach, this research presents novel advancement in ransomware detection by utilizing network file system indicators of compromise while ensuring privacy. The methodology involves the decentralized training machine learning models across multiple clients, which enhances model's robustness adaptability various attack scenarios. Extensive experiments evaluations demonstrate high accuracy, precision, recall, F1-scores achieved proposed model, showcasing its effectiveness real-world applications. innovative combination preprocessing, feature engineering, sophisticated techniques within framework results scalable privacy-preserving solution capable addressing dynamic evolving landscape threats. This study contributes valuable insights into development effective systems, emphasizing importance collaborative enhancing cybersecurity defenses.

Language: Английский

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Federated RNN-Based Detection of Ransomware Attacks: A Privacy-Preserving Approach

Published: Aug. 13, 2024

The rise of ransomware as a predominant cybersecurity threat has necessitated the development innovative detection mechanisms capable adapting to rapidly evolving nature such attacks. In response this challenge, federated learning, combined with Recurrent Neural Networks (RNNs), offers novel approach that preserves data privacy while maintaining high accuracy. research presented explores implementation learning framework, where RNN models are trained across decentralized datasets without sharing sensitive data, ensuring compliance regulations. Through comprehensive experiments, study demonstrates model achieves comparable performance centralized models, added benefit enhanced security. results demonstrate potential scalable and robust solution for applications, particularly in environments confidentiality is paramount. findings further highlight broader implications adopting techniques privacy-preserving machine paving way future advancements secure effective detection.

Language: Английский

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Opcode Memory Analysis: A Data-Centric Machine Learning Framework for Early Detection and Attribution of Ransomware

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 21, 2024

Ransomware has emerged as one of the most significant threats in cybersecurity landscape, causing widespread disruption and financial loss across various sectors. To address growing sophistication ransomware attacks, a novel machine learning framework leveraging opcode memory analysis been developed, enabling early detection accurate attribution ransomware. Through systematic examination low-level operational instructions within system memory, proposed model distinguishes itself from traditional approaches by providing more intrinsic understanding malware behavior, leading to enhanced accuracy ability identify specific families. The model's architecture, which includes dual-output mechanism for simultaneous attribution, demonstrates scalability applicability diverse environments. Extensive experimental results indicate that this approach not only surpasses existing methods terms performance but also offers robust solution real-time threat mitigation. findings demonstrate potential critical component development next-generation defenses, contributing resilient proactive protective measures against evolving threats.

Language: Английский

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Double-sided Information Asymmetry in Double Extortion Ransomware

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 22, 2024

Double extortion ransomware attacks are a form of cyber attack where the victims files both encrypted and exfiltrated for purposes. There is empirical evidence that double leads to an increased willingness pay ransom, higher ransoms, compared encryption-only attacks. In this paper we model two important sources assymetric information between victim attacker: (a) Victims typically uncertain whether data exfiltrated, due example misconfigured monitoring systems. (b) It hard attackers estimate value compromised files. We use game theory analyse payoff consequences such private information. Specifically, signaling with double-sided asymmetry: (1) know do not, (2) if it but not. Our analysis indicates substantially lowers attackers. interpretation, suggests valuable means reduce incentives criminals pursue ransomware.

Language: Английский

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Detection of Stealthy Encryption in Ransomware Using AI-Driven Anomaly Detection Models

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 23, 2024

Ransomware continues to pose a significant threat cybersecurity, with increasingly sophisticated techniques allowing malicious actors evade traditional detection mechanisms and inflict substantial damage on both individual organizational levels. The introduction of an AI-driven model that integrates anomaly supervised learning offers novel approach identifying ransomware activities, particularly those utilizing stealthy encryption are designed avoid detection. Through comprehensive evaluation, the proposed has demonstrated superior performance compared existing methods, achieving higher accuracy, reduced false positives, enhanced resilience against adversarial evasion. model's scalability efficiency across diverse operational environments further demonstrate its practical applicability, making it viable solution for real-time in high-performance resource-constrained settings. research contributes ongoing efforts fortify cybersecurity defenses by offering robust, adaptable, scalable framework capable addressing evolving nature threats.

Language: Английский

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Integrated Detection and Mitigation of Linux-based Ransomware through Machine Learning Analysis of File Access Patterns and Security Logs

Published: Aug. 20, 2024

Cybersecurity challenges continue to escalate as ransomware attacks become more frequent and sophisticated, posing significant risks both individual organizational data integrity. The development of an integrated detection mitigation system presents a novel approach, enhancing the responsiveness effectiveness cyber defenses through real-time analysis automated response mechanisms. This article details design, implementation, evaluation such system, demonstrating its superiority in accuracy speed compared existing solutions. Through rigorous testing under simulated conditions, not only meets but often exceeds current industry standards for threat management. Future enhancements are discussed, emphasizing potential further advancements adaptive cybersecurity measures.

Language: Английский

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On Countering Ransomware Attacks Using Strategic Deception

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 149 - 176

Published: Oct. 10, 2024

Language: Английский

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A Novel Quantum-Backed Decision Vector Framework for Ransomware Detection Using Nonlinear Signal Entropy Mapping

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 25, 2024

The increasing complexity and sophistication of modern cyber threats need innovative detection mechanisms capable adapting to rapidly evolving attack vectors. A quantum-inspired framework was introduced address the challenges identifying ransomware through advanced decision-making algorithms nonlinear entropy analysis. integration mapping allowed system capture subtle deviations in behavior, facilitating early-stage identification malicious activities. Quantum decision vectors provided a robust mechanism for evaluating classifying patterns across diverse datasets without relying on static signatures. Experimental evaluations demonstrated superior performance accuracy, latency, resource efficiency compared traditional heuristic machine learning-based methods. Polymorphic variants, often evading conventional approaches, were effectively detected framework's generalized analytical capabilities. exhibited adaptability imbalanced datasets, maintaining high reliability precision varying distributions benign Results highlighted its computational efficiency, with significantly reduced demands, enabling deployment resource-constrained high-throughput environments. modular design supports scalability existing cybersecurity infrastructures. Comprehensive analysis revealed substantial reductions false positive rates, enhancing automated processes. study underscores practical viability theoretical contributions methodologies improving defenses.

Language: Английский

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Automated Ransomware Detection Using Hierarchical Encryption Deviation Analysis

Published: Dec. 2, 2024

The increasing reliance on encryption in cyberattacks has highlighted the urgent need for detection systems capable of addressing sophisticated adversarial techniques. A multi-layered approach known as Hierarchical Encryption Deviation Analysis (HEDA) was developed, offering precise anomaly through analysis cryptographic deviations across hierarchical layers. framework achieved high accuracy, exceeding 92\% modern ransomware variants, including LockBit, Hive, BlackCat, and Conti, while maintaining low false positive rates, particularly benign encrypted files. Its scalability demonstrated stress tests involving large datasets, where minimal latency resource usage ensured compatibility with real-time operational requirements. comparative evaluation against signature-based behavior-based revealed superior performance detecting polymorphic adversarially crafted samples. modular system design enabled seamless integration into existing security infrastructures, energy-efficient processing addressed sustainability concerns enterprise environments. Experimental results further system’s robustness high-bandwidth network conditions, rapid adaptability were maintained varying levels traffic. Through detailed analysis, effectively isolated malicious behaviors, even cases complex schemes randomness. study also emphasized practicality integrating techniques machine learning models to provide a scalable adaptable solution mitigation. By focusing structures, methodology supports comprehensive patterns, ensuring robust capabilities diverse scenarios. findings contribute significantly advancing field cybersecurity, actionable strategies combating encryption-based threats an increasingly hostile cyber landscape.

Language: Английский

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