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<p>The rapid integration of Internet of Things (IoT) devices into various domains has led to heightened concerns regarding cybersecurity threats. IoT networks, characterized by their heterogeneous and resource-constrained nature, face numerous challenges in effectively detecting and mitigating intrusions. Consequently, there is a critical need for lightweight Intrusion Detection Systems (IDS) tailored for these environments. This paper introduces a novel lightweight IDS designed specifically for IoT environments. Our approach utilizes a mean thresholding-based Fast Correlation-Based Filter (mFCBF) algorithm for feature selection, enhancing efficiency while maintaining detection accuracy. Leveraging the power of LightGBM and XGBoost for classification, our proposed IDS demonstrates exceptional effectiveness in identifying malicious activities within IoT networks. Through extensive experimentation on benchmark datasets such as CICIDS 2017, NSL-KDD and BoT-IoT we validate the efficacy of our approach, achieving 99.34% accuracy for CICIDS 2017, 99.62% for NSL-KDD and 99.75% for BoT-IoT while maintaining an average CPU consumption of just 2.03% and Memory usages of approximately 1.2%. These results demonstrate that our IDS is both lightweight and robust, capable of effectively safeguarding against emerging threats.</p>

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mFCBF based lightweight intrusion detection system for IoT networks

  • 期刊论文

  • 发表日期：

    2025年7月30日

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  • Jai Prakash Kushwaha,
  • Saumya Bhadauria,
  • Shashikala Tapaswi

摘要

The rapid integration of Internet of Things (IoT) devices into various domains has led to heightened concerns regarding cybersecurity threats. IoT networks, characterized by their heterogeneous and resource-constrained nature, face numerous challenges in effectively detecting and mitigating intrusions. Consequently, there is a critical need for lightweight Intrusion Detection Systems (IDS) tailored for these environments. This paper introduces a novel lightweight IDS designed specifically for IoT environments. Our approach utilizes a mean thresholding-based Fast Correlation-Based Filter (mFCBF) algorithm for feature selection, enhancing efficiency while maintaining detection accuracy. Leveraging the power of LightGBM and XGBoost for classification, our proposed IDS demonstrates exceptional effectiveness in identifying malicious activities within IoT networks. Through extensive experimentation on benchmark datasets such as CICIDS 2017, NSL-KDD and BoT-IoT we validate the efficacy of our approach, achieving 99.34% accuracy for CICIDS 2017, 99.62% for NSL-KDD and 99.75% for BoT-IoT while maintaining an average CPU consumption of just 2.03% and Memory usages of approximately 1.2%. These results demonstrate that our IDS is both lightweight and robust, capable of effectively safeguarding against emerging threats.