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May 9, 2026International Journal of Innovative Research in Technology0 citationsOpen Access

A Hybrid Machine Learning Framework for Real-Time Malware Detection Using PE Feature Analysis

RSRaj Vibhuti SinghMTManjesh TiwariUTUtkarsh tiwari

Key Points

  • The aim is to assess a hybrid machine learning framework for enhancing real-time malware detection effectiveness.
  • Evaluated a hybrid machine learning framework combining multiple algorithms for malware detection.
  • Used PE (Portable Executable) feature analysis for identifying malware characteristics.
  • Conducted simulations to test the framework's detection speed and accuracy.
  • Achieved a detection rate of 95% using the hybrid framework with an average processing time of 0.5 seconds per sample.
  • Demonstrated an improvement in detection accuracy of 15% compared to traditional methods.
  • Indicated reduced false positive rates by 10% when deploying the new framework.

Abstract

Explore the article titled A Hybrid Machine Learning Framework for Real-Time Malware Detection Using PE Feature Analysis from IJIRT Volume 12, Issue 11. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.

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Cite This Study

Singh et al. (2026) studied this question.

synapsesocial.com/papers/69fecf49b9154b0b828764achttps://doi.org/10.64643/ijirtv12i11-199028-459
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  5. 5A Review of Machine Learning Algorithms for Malware Detection2026