Malware Recognition Using Machine Learning Methods Based on Semantic Behaviors

Authors

  • Praveen Hugar Assistant Professor, Department of Information Technology, J B Institute of Engineering and Technology, Moinabad, India Author
  • Mayur Pershad Students, Department of Information Technology, J B Institute of Engineering and Technology, Moinabad, India Author
  • T Sathvika Students, Department of Information Technology, J B Institute of Engineering and Technology, Moinabad, India Author
  • Ganesh Bhukya Students, Department of Information Technology, J B Institute of Engineering and Technology, Moinabad, India Author

Keywords:

Machine Learning, Computer Security, Malware Recognition

Abstract

 Malware is any programme that gains access  to or instals itself on a computer without the permission of  the system's administrators. For cyber-criminals to achieve  their nefarious objectives and purposes, a variety of viruses  has been widely deployed. To tackle the growing number of  malicious programmes and lessen their hazard, a novel deep  learning framework is developed that employs NLP  approaches as a starting point and combines CNN and LSTM  neurones to record locally spatial correlations and learn from  sequential longterm dependencies. As a result, for the  malware classification job, high-level abstractions and  representations are automatically derived. The accuracy of  categorization rises from 0.81 (best by Random Forest) to  about 1.0. 

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References

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Published

2022-06-30

Issue

Vol. 9 No. 3 (2022): International Journal of Innovative Research in Engineering & Management (June) 2022

Section

Articles

How to Cite

Malware Recognition Using Machine Learning Methods Based on Semantic Behaviors . (2022). International Journal of Innovative Research in Engineering & Management, 9(3), 41–44. Retrieved from https://acspublisher.com/journals/index.php/ijirem/article/view/10880