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The growing use of personal computers and Android devices has increased the demand for better protection against malware, a harmful software designed to disrupt, gain, or damage unauthorized access to systems. Traditional detection techniques, such as signature-based methods, often fall short when facing new and advanced malware threats. This paper explores how deep learning and machine learning techniques can enhance malware detection. A variety of ML algorithms, including Naïve Bayes, Decision Trees, and Support Vector Machines, are compared with DL models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM). The research utilizes a dataset comprising both malware and benign files, demonstrating that DL models perform better in recognizing complex patterns associated with malware, leading to higher accuracy rates in detection.

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Leveraging Machine Learning and Deep Learning for Enhanced Malware Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Nandan Dargad,
  • Omkar Mantri,
  • Arti Patil,
  • R. B. Chandrayan

摘要

The growing use of personal computers and Android devices has increased the demand for better protection against malware, a harmful software designed to disrupt, gain, or damage unauthorized access to systems. Traditional detection techniques, such as signature-based methods, often fall short when facing new and advanced malware threats. This paper explores how deep learning and machine learning techniques can enhance malware detection. A variety of ML algorithms, including Naïve Bayes, Decision Trees, and Support Vector Machines, are compared with DL models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM). The research utilizes a dataset comprising both malware and benign files, demonstrating that DL models perform better in recognizing complex patterns associated with malware, leading to higher accuracy rates in detection.