Ransomware Detection on Linux Using Machine Learning with Random Forest Algorithm

Published: June 7, 2024

Ransomware continues to pose a significant threat cybersecurity, particularly affecting critical systems running on Linux. The novel application of the random forest algorithm for detecting ransomware Linux offers advancement, leveraging machine learning enhance detection accuracy and adaptability. methodology involved collecting diverse dataset samples benign files, followed by meticulous feature extraction robust model. Performance evaluation demonstrated high precision, recall, overall accuracy, surpassing existing methods such as support vector machines neural networks. Comparative analysis highlighted model’s superior ability handle high-dimensional data manage complex interactions, resulting in more reliable accurate detection. Despite computational complexity extensive preprocessing requirements, findings underscore potential significantly improve cybersecurity measures against ransomware. comprehensive provides valuable insights into development effective mechanisms, affirming algorithm’s pivotal role mitigating threats systems.

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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Efficient Ransomware Detection through Dynamic File System Traffic Analysis: A Methodological Approach

Published: Oct. 7, 2024

Ransomware continues to evolve as one of the most severe threats modern digital infrastructures, frequently bypassing traditional security mechanisms through increasingly sophisticated obfuscation techniques. A novel approach for combating ransomware leverages real-time dynamic file system traffic analysis detect malicious behaviors before significant damage is inflicted. The proposed operates continuous monitoring events and process interactions, classifying activity either benign or ransomware-related machine learning models trained on feature-rich datasets. This demonstrates substantial improvements in detection accuracy, especially against zero-day variants, efficiently reduces both false positives negatives. Furthermore, maintains low computational overhead, making it suitable deployment environments requiring protection. Through its ability adapt new without manual updates, offers a scalable effective solution detection, providing robust defense enterprise resource-constrained environments.

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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Advanced Autonomous Detection of Ransomware Using Dynamic Crypto-Entropy Signature Analysis

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

Published: Nov. 15, 2024

The sophistication of cyber threats requires the development advanced detection mechanisms capable identifying and mitigating malicious activities with minimal human intervention. Dynamic Crypto-Entropy Signature Analysis (DCESA) framework introduces an autonomous approach to ransomware through analysis cryptographic entropy patterns inherent in encryption behaviors. Through dynamically generating unique signatures, DCESA effectively distinguishes between benign activities, thereby enhancing accuracy reducing false positives. Empirical evaluations have demonstrated DCESA's proficiency a diverse array strains, including previously unseen variants, impact on system performance. integration into cybersecurity infrastructures offers proactive efficient solution for attacks, overall security posture organizations.

Language: Английский

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Dynamic Polyvariant Heuristics for Autonomous Ransomware Detection

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

Published: Nov. 25, 2024

The escalating frequency and sophistication of ransomware attacks necessitate innovative detection methodologies. Traditional signature-based systems often falter against novel polymorphic strains. Dynamic Polyvariant Heuristics (DPH) emerges as a approach, integrating dynamic analysis with machine learning to enhance capabilities. DPH systematically monitors system behaviors, extracting features indicative activity, such anomalous file modifications network communications. These inform classifiers trained on diverse datasets, enabling the identification both known emerging variants. Empirical evaluations demonstrate DPH's high accuracy, low false positive rates, rapid response times, showing its potential for real-time threat mitigation. system's adaptability evolving tactics further highlights robustness. Comparative analyses reveal superiority over existing methods, particularly in handling zero-day attacks. integration adaptive components allows continuous model updates, maintaining efficacy threats. findings suggest that offers significant advancement methodologies, contributing broader field cybersecurity.

Language: Английский

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