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Intrusion Detection in IoT Devices Using ML and DL Models with Fisher Score Feature Selection

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Cryptology and Network Security with Machine Learning (ICCNSML 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 918))

  • 76 Accesses

Abstract

IoT devices are physical things that have sensors, network connectivity, and software built into them. This allows them to gather data and share it with other systems and devices. The rising usage of Internet of Things (IoT) systems in critical infrastructure and industrial settings has raised concerns about their vulnerability to cyber-attacks. Therefore, the IIoT desperately needs techniques for enhancing strategic actions. In this work, we propose an Intrusion Detection System (IDS) for IoT devices by using deep learning (DL) and machine learning (ML) algorithms, incorporating Fisher score feature selection on the Edge-IIoT dataset. To develop effective IDS models, we have first applied the Fisher score feature selection method for the identification of the most discriminative features from the Edge-IIoT dataset. For the ML-based IDS, we employ decision tree and random forest models, optimizing their hyperparameters using a systematic hyperparameter tuning approach getting 93.7% (Decision Tree), 94.36% (Random Forest), and 94.5% (Random Forest with hyperparameter tuning) accuracy. For the DL-based IDS, we propose a single-layered feed forward neural network (FFNN) and a multi-layered feed forward neural network (MLFNN) getting 96.1% and 96.5% accuracy, respectively. These models are trained on the selected features from the dataset and evaluated using various performance indicators, including recall, F1-score, accuracy, and precision. Overall, this research work contributes to the field of intrusion detection in IoT devices by combining Fisher score feature selection with both ML and DL algorithms. The findings highlight the prospective of hybrid approaches in increasing the security and resilience of IoT systems, ultimately enabling more robust and efficient intrusion detection mechanisms for IoT deployments in critical domains.

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Author information

Authors and Affiliations

  1. IGDTUW, Kashmere Gate, Delhi, India

    Deeksha Rajput & Deepak Kumar Sharma

  2. MSCW, University of Delhi, Delhi, India

    Megha Gupta

Authors
  1. Deeksha Rajput
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  2. Deepak Kumar Sharma
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  3. Megha Gupta
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Correspondence to Deeksha Rajput .

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Editors and Affiliations

  1. Department of Mathematics, Pranveer Singh Institute of Technology, Kanpur, India

    Atul Chaturvedi

  2. Department of Mathematics, Indian Institute of Technology Jammu, Jammu, India

    Sartaj Ul Hasan

  3. Applied Statistics Unit, Indian Statistical Institute, Kolkata, India

    Bimal Kumar Roy

  4. Department of Mathematics, Bar-Ilan University, Ramat-Gan, Israel

    Boaz Tsaban

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Rajput, D., Sharma, D.K., Gupta, M. (2024). Intrusion Detection in IoT Devices Using ML and DL Models with Fisher Score Feature Selection. In: Chaturvedi, A., Hasan, S.U., Roy, B.K., Tsaban, B. (eds) Cryptology and Network Security with Machine Learning. ICCNSML 2023. Lecture Notes in Networks and Systems, vol 918. Springer, Singapore. https://doi.org/10.1007/978-981-97-0641-9_8

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  • DOI: https://doi.org/10.1007/978-981-97-0641-9_8

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