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Enhancing Intrusion Detection in Intelligent Internet of Medical Things

초록/요약

The Internet of Things (IoT) has permeated various industries, including health- care, through the Internet of Medical Things (IoMT). IoMT enables remote patient monitoring by incorporating internet connectivity into healthcare systems, enhancing patient care via real-time data collection and interaction. Although IoMT provides numerous advantages, its proliferation raises substantial security concerns due to interconnected systems’ high vulnerability. Although innovative, IoMT is vulnerable to a range of security risks that could compromise patient data confidentiality, privacy, and overall integrity of healthcare services. Thus, it is important to implement strong security mechanisms to prevent unauthorized access and cyberattacks. The extensive amount of data generated by IoT systems, which often involves numerous devices, can result in high-dimensional datasets that surpass traditional processing and analysis techniques. Developed to handle such data, complex models are often prone to overfit- ting, difficult to interpret, and require extensive computational resources. Therefore, effective feature engineering is crucial for simplifying these models and enhancing their performance while reducing computational load and storage requirements. To address these challenges, we propose a feature engineering method for an innovative intrusion detection system (IDS) tailored for IoMT. This system uses a sensitivity factor calcu- lation for categorical features and weighted empirical distribution ranking (EDR) to conduct feature selection. By selecting optimal features based on the inverse frequency weights of their empirical distributions, our approach aimed to streamline the data analysis process. Our method was rigorously tested using the WUSTL-EHMS, MQTT- IoT-IDS, and CICIoMT2024 datasets across four AI/ML models, demonstrating its efficacy in detecting security risks in IoMT environments. The proposed IDS achieved high accuracies of 96%, 91%, and 95.3% for the WUSTL-EHMS, MQTT-IoT-IDS, and CICIoMT2024 datasets, respectively. The average improvement compared to the base method was 80% in the WUSTL-EHMS dataset and 41% for the CICIoMT dataset. Moreover, the proposed method significantly reduced the training and detec- tion times compared with other methods. This performance highlights the potential of the proposed approach to satisfy the requirements of various IoMT applications.

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목차

I. Introduction 1
1.1 IoT and its Role in Healthcare Sector 1
1.1.1 Security Challenges and Intrusion Detection in IoMT 3
1.1.2 The Role of Machine Learning in Healthcare and Cybersecurity 5
1.1.3 Enhancing IDS with Feature Selection 6
1.2 Motivation 7
II. Background and Related Works 9
2.1 Traditional IDS Model for IoMT 9
2.2 Related works 10
2.2.1 Feature Selection and Extraction 10
2.2.2 Machine Learning Algorithms for IDS 11
III. System Design 14
3.1 System Architecture Overview 14
3.1.1 Feature engineering 14
3.1.2 Machine Learning Models 15
3.2 Proposed Method for Feature Engineering 19
3.2.1 Sensitivity Factor 20
3.2.2 Weighted EDR-based feature selection 23
IV. Performance evaluation 25
4.1 Dataset description and model hyperparameters 25
4.2 Evaluation metrics 28
4.3 Results with different datasets 30
4.3.1 WUSTL-EHMS 30
4.3.2 MQTT-IoT-IDS 33
4.3.3 CICIoMT2024 34
4.4 Comparison results with selected related works 36
V. Conclusion and Future Works 38
References 39

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