Enhanced Multigrained Scanning-Based Deep Stacking Network for Intrusion Detection in IoMT Networks
In light of the flourishing proliferation of internet services, the popularity of the Internet of Things (IoT) has swiftly grown in the medical and healthcare fields, and this has been accompanied by a simultaneous escalation in the sophistication of intrusion attacks. Drawing inspiration from the a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10716376/ |
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| Summary: | In light of the flourishing proliferation of internet services, the popularity of the Internet of Things (IoT) has swiftly grown in the medical and healthcare fields, and this has been accompanied by a simultaneous escalation in the sophistication of intrusion attacks. Drawing inspiration from the accomplishments of deep learning in cyber threat detection, we propose a multigrained scanning-based deep stacking network (MGDSN) to defend against sophisticated cyberattacks on Internet of Medical Things (IoMT) networks. To address the obscured characteristics of intricate cyberattacks, the MGDSN incorporates four components. First, the feature augmentation process leverages an improved multigrained scanning technique to enhance discriminative information. Second, a deep stacking network (DSN) with a weighting mechanism is employed to generate a set of predictive results for making the final decision. Third, a meta-classifier is introduced to scrutinize the influence of the predictive results when producing the final decision and exploiting a set of meaningfully extracted features. Finally, a loss function is properly designed to take both the predictive losses of the DSN modules and the final predictive loss into account. The outstanding performance achieved by the MGDSN is confirmed through comprehensive evaluations comparing it with the state-of-the-art techniques, encompassing metrics such as the accuracy, precision, recall, F1 score, Cohen’s kappa coefficient, Matthews correlation coefficient, and area under the curve achieved on the IoMT datasets. The MGDSN exhibits a notable improvement ranging from approximately 0.12%-329.21%. |
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| ISSN: | 2169-3536 |