SIGNATURELESS RANSOMWARE DETECTION USING LSTM

Registration ID: IJNRD_222849

Published ID: IJNRD2406041

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Yaswanthraj S, Ajaykumar JS, Yuvan krishna P, Thirukumaran Y, Arun khrishna S,Dharun J

Ransomware · Cyber-security · Protection mechanism · Data security · Malware detection

In cybersecurity, the ongoing threat of malicious software or malware poses a significant challenge to the integrity of computer systems, often beyond conventional detection methods Behavior types of sequences include LSTM- The model-based model is trained. This series masks subtle interactions in software systems, allowing the model to distinguish between non-optimal and malignant patterns. By analyzing dynamic objects such as system calls and API calls, the model can identify subtle nuances of malware behavior that static methods may overlook Detailed data sets, including labeled examples of benign and malicious software behavior of, are carefully collected, preprocessed, for model training and analysis Comparative analysis is performed against traditional handwriting-based methods, traditional machine learning classifiers, and other deep learning algorithms to assess the performance of LSTM models used is effective In conclusion, the integration of LSTM-based models in sophisticated and flexible cybersecurity solutions offers a promising approach to grow This study contributes drive ongoing efforts to develop robust and adaptive security solutions, which are necessary to protect digital assets in today’s ever-changing threat landscape.

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Yaswanthraj S, Ajaykumar JS, Yuvan krishna P, Thirukumaran Y, & Arun khrishna S,Dharun J (June-2024). SIGNATURELESS RANSOMWARE DETECTION USING LSTM. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(6), a399-a405. https://ijnrd.org/papers/IJNRD2406041.pdf

Volume 9 Issue 6, June-2024

Pages : a399-a405

Paper Reg. ID: IJNRD_222849

Published Paper Id: IJNRD2406041

Downloads: 000122253

Research Area: Computer Engineering 

Author Type: Indian Author

Country: Coimbatore, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2406041.pdf

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