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

Raj  Vibhuti Singh; Manjesh Tiwari; Utkarsh tiwari; Prof Tarunima Mukherjee

doi:10.64643/ijirtv12i11-199028-459

April 23, 2026International Journal of Innovative Research in TechnologyOpen Access

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

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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.

Implication

Evaluates real-time malware detection effectiveness in women, suggesting a novel approach using machine learning.

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/69fecf49b9154b0b828764ac https://doi.org/10.64643/ijirtv12i11-199028-459

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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