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

EasyChair Preprint no. 12034

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, deep learning, ensemble learning, feature engineering, hybrid models, machine learning, malware detection, Security, Threat Intelligence

@Booklet{EasyChair:12034,
  author = {Jonny Bairstow},
  title = {Enhancing Cybersecurity: Leveraging Ensemble Learning for Effective Malware Detection and Classification},
  howpublished = {EasyChair Preprint no. 12034},

  year = {EasyChair, 2024}}