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<p>The proliferation of smart home devices, while enhancing convenience, introduces new security vulnerabilities within the ever-expanding Internet of Things. These resource-constrained devices generate complex data, further complicated by limited labeled data, hindering traditional supervised learning approaches. To address these challenges, this research proposes a novel Anomaly Intrusion Detection System framework for smart home environments. This framework leverages the distributed processing capabilities of the edge-fog-cloud architecture, where a one-class asymmetric stacked autoencoder plays a crucial role. This deep learning model performs efficient dimensionality reduction, transforming complex, high-dimensional data into a lower-dimensional representation and facilitating efficient processing on resource-constrained smart home devices. The proposed model utilizes unsupervised learning to detect anomalies and suspicious activities without requiring extensive labeled data, making it well-suited for dynamic IoT environments. The proposed framework is evaluated using two real-world datasets, BoT-IoT and IoT-23, demonstrating exceptional performance. It achieved a&#xa0;high accuracy of 99.9970% and 97.7284%, with a&#xa0;precision of 99.9987% and 97.5610%, recall of 99.9983% and 97.5401%, and F1-scores of 99.9985% and 97.5505%, respectively. The model also exhibited a low false positive rate of 0.10% and 0.02%, along with a high Area Under the ROC Curve of 99.7559% and 98.3235%. Additionally, the Matthews Correlation Coefficient values of 0.8881 and 0.9543 indicate a balanced classification between normal and attack traffic. The framework demonstrated efficient processing times, with training durations of 9.9349 and 237.2206 seconds and significantly faster testing times of 0.1936 and 0.4504 seconds for the two datasets. Preprocessing times were also minimal at 55.8496 and 0.0973 seconds, confirming the model’s suitability for real-time anomaly detection. These results validate the effectiveness and scalability of the proposed&#xa0;model-for enhancing security in smart home IoT networks.</p>

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Fog-empowered anomaly detection in IoT networks using one-class asymmetric stacked autoencoder

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月23日

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  • Aya G. Ayad,
  • Nehal A. Sakr,
  • Noha A. Hikal

摘要

The proliferation of smart home devices, while enhancing convenience, introduces new security vulnerabilities within the ever-expanding Internet of Things. These resource-constrained devices generate complex data, further complicated by limited labeled data, hindering traditional supervised learning approaches. To address these challenges, this research proposes a novel Anomaly Intrusion Detection System framework for smart home environments. This framework leverages the distributed processing capabilities of the edge-fog-cloud architecture, where a one-class asymmetric stacked autoencoder plays a crucial role. This deep learning model performs efficient dimensionality reduction, transforming complex, high-dimensional data into a lower-dimensional representation and facilitating efficient processing on resource-constrained smart home devices. The proposed model utilizes unsupervised learning to detect anomalies and suspicious activities without requiring extensive labeled data, making it well-suited for dynamic IoT environments. The proposed framework is evaluated using two real-world datasets, BoT-IoT and IoT-23, demonstrating exceptional performance. It achieved a high accuracy of 99.9970% and 97.7284%, with a precision of 99.9987% and 97.5610%, recall of 99.9983% and 97.5401%, and F1-scores of 99.9985% and 97.5505%, respectively. The model also exhibited a low false positive rate of 0.10% and 0.02%, along with a high Area Under the ROC Curve of 99.7559% and 98.3235%. Additionally, the Matthews Correlation Coefficient values of 0.8881 and 0.9543 indicate a balanced classification between normal and attack traffic. The framework demonstrated efficient processing times, with training durations of 9.9349 and 237.2206 seconds and significantly faster testing times of 0.1936 and 0.4504 seconds for the two datasets. Preprocessing times were also minimal at 55.8496 and 0.0973 seconds, confirming the model’s suitability for real-time anomaly detection. These results validate the effectiveness and scalability of the proposed model-for enhancing security in smart home IoT networks.