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This paper investigates the efficiency of memory-optimized machine learning models using three classifiers: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Gradient Boosting Machines (GBMs). Through detailed experiments, we assess their accuracy, precision, recall, and F1 scores to determine which model performs best. The results indicate that GBM outperforms the other models, achieving an accuracy of 96.74%. Readers will be provided with information on the methods used, the advantages of each classifier, and the reasons behind GBM’s enhanced performance.

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Static Malware Analysis with Machine Learning Models for IoT

  • 会议论文

  • 发表日期：

    2026年

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  • Sindhu Vukkurthy,
  • Neelam Dayal

摘要

This paper investigates the efficiency of memory-optimized machine learning models using three classifiers: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Gradient Boosting Machines (GBMs). Through detailed experiments, we assess their accuracy, precision, recall, and F1 scores to determine which model performs best. The results indicate that GBM outperforms the other models, achieving an accuracy of 96.74%. Readers will be provided with information on the methods used, the advantages of each classifier, and the reasons behind GBM’s enhanced performance.