Scholarlink

With the rapid expansion of Internet of Things (IoT) and smart device ecosystems, security threats such as malware attacks have become a critical concern. Traditional signature-based malware detection methods struggle to detect evolving and polymorphic threats, necessitating the development of intelligent, data-driven cybersecurity mechanisms. This study proposes a novel malware detection framework that integrates optimized feature engineering and deep neural networks (DNNs) to classify malware in smart devices with high precision. The approach focuses on behavioral feature extraction, including API call sequences, network activity logs, and application permissions, followed by feature selection techniques to reduce dimensionality while retaining key discriminative attributes. A comparative analysis of various machine learning (ML) models, including Random Forest, Support Vector Machine (SVM), and Deep Learning models, demonstrates that the proposed feature engineering-enhanced DNN model achieves 96.1% accuracy, outperforming conventional methods. Extensive experimentation on a real-world dataset of 10,000 smart device applications showcases the robustness and scalability of our framework. This research contributes to enhancing security in smart environments by providing an adaptive and computationally efficient malware detection system.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Smart Malware Detection in IoT Devices Using Optimized Feature Engineering and Deep Neural Networks

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • A. Punidha,
  • E. Arul,
  • E. Yuvarani,
  • S. Rajasakaran

摘要

With the rapid expansion of Internet of Things (IoT) and smart device ecosystems, security threats such as malware attacks have become a critical concern. Traditional signature-based malware detection methods struggle to detect evolving and polymorphic threats, necessitating the development of intelligent, data-driven cybersecurity mechanisms. This study proposes a novel malware detection framework that integrates optimized feature engineering and deep neural networks (DNNs) to classify malware in smart devices with high precision. The approach focuses on behavioral feature extraction, including API call sequences, network activity logs, and application permissions, followed by feature selection techniques to reduce dimensionality while retaining key discriminative attributes. A comparative analysis of various machine learning (ML) models, including Random Forest, Support Vector Machine (SVM), and Deep Learning models, demonstrates that the proposed feature engineering-enhanced DNN model achieves 96.1% accuracy, outperforming conventional methods. Extensive experimentation on a real-world dataset of 10,000 smart device applications showcases the robustness and scalability of our framework. This research contributes to enhancing security in smart environments by providing an adaptive and computationally efficient malware detection system.