Paper Title
SIGNATURELESS RANSOMWARE DETECTION USING LSTM
Article Identifiers
Authors
Yaswanthraj S , Ajaykumar JS , Yuvan krishna P , Thirukumaran Y , Arun khrishna S,Dharun J
Keywords
Ransomware · Cyber-security · Protection mechanism · Data security · Malware detection
Abstract
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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How To Cite (APA)
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
Issue
Volume 9 Issue 6, June-2024
Pages : a399-a405
Other Publication Details
Paper Reg. ID: IJNRD_222849
Published Paper Id: IJNRD2406041
Downloads: 000122008
Research Area: Computer EngineeringÂ
Country: Coimbatore, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2406041.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2406041
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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