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Enhancing Cybersecurity: Leveraging Ensemble Learning for Effective Malware Detection and Classification

EasyChair Preprint 12034

12 pagesDate: February 12, 2024

Abstract

In an era dominated by escalating cyber threats, the need for robust malware detection and classification systems has become imperative. This paper introduces a novel approach to enhance cybersecurity through the integration of ensemble learning techniques for more effective detection and classification of malware. Ensemble learning combines the strengths of multiple machine learning models to improve overall accuracy and reliability. The proposed system leverages this approach to fortify the defenses against constantly evolving and sophisticated malware attacks. Through comprehensive experimentation, the results demonstrate the superior performance of the ensemble learning-based solution in comparison to traditional methods.

Keyphrases: Classification, Cybersecurity, Security, Threat Intelligence, deep learning, ensemble learning, feature engineering, hybrid models, machine learning, malware detection

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:12034,
  author    = {Jonny Bairstow},
  title     = {Enhancing Cybersecurity: Leveraging Ensemble Learning for Effective Malware Detection and Classification},
  howpublished = {EasyChair Preprint 12034},
  year      = {EasyChair, 2024}}
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