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Evolving random weight neural networks based on oversampled-segmented examples for IoT intrusion detection

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Abstract

The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results.

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Data Availability

All data analyzed during this study are included in the article by [8] and are available at the repository: https://sites.google.com/view/iot-network-intrusion-dataset/home.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. School of Computing and informatics, Al Hussein Technical University, Amman, 11831, Jordan

    Raneem Qaddoura

  2. King Abdullah II School of Information Technology, The University of Jordan, Amman, 11942, Jordan

    Hossam Faris

  3. Research Centre for Information and Communications Technologies, University of Granada, Granada, Spain

    Hossam Faris

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  1. Raneem Qaddoura
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Contributions

Conceptualization, H.F. and R.Q.; methodology, H.F. and R.Q.; validation, R.Q.; data curation, R.Q.; writing---original draft preparation, R.Q.; writing---review and editing, R.Q. and H.F.; supervision H.F.; project administration, H.F and R.Q.

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Correspondence to Raneem Qaddoura.

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Qaddoura, R., Faris, H. Evolving random weight neural networks based on oversampled-segmented examples for IoT intrusion detection. J Supercomput 80, 16393–16427 (2024). https://doi.org/10.1007/s11227-024-06071-3

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