Securing Healthcare with Deep Learning: A CNN-Based Model for medical IoT Threat Detection (2410.23306v2)

Abstract: The increasing integration of the Internet of Medical Things (IoMT) into healthcare systems has significantly enhanced patient care but has also introduced critical cybersecurity challenges. This paper presents a novel approach based on Convolutional Neural Networks (CNNs) for detecting cyberattacks within IoMT environments. Unlike previous studies that predominantly utilized traditional ML models or simpler Deep Neural Networks (DNNs), the proposed model leverages the capabilities of CNNs to effectively analyze the temporal characteristics of network traffic data. Trained and evaluated on the CICIoMT2024 dataset, which comprises 18 distinct types of cyberattacks across a range of IoMT devices, the proposed CNN model demonstrates superior performance compared to previous state-of-the-art methods, achieving a perfect accuracy of 99% in binary, categorical, and multiclass classification tasks. This performance surpasses that of conventional ML models such as Logistic Regression, AdaBoost, DNNs, and Random Forests. These findings highlight the potential of CNNs to substantially improve IoMT cybersecurity, thereby ensuring the protection and integrity of connected healthcare systems.

• The paper presents a novel CNN-based model achieving 99% accuracy in distinguishing benign from harmful network traffic in IoMT environments.
• It employs a multi-layer architecture—including preprocessing, convolutional, and fully connected layers—on the CICIoMT2024 dataset covering 18 cyberattack types.
• The model's superior multiclass classification performance underscores its potential for real-time IoMT threat monitoring, despite its high computational demands.

Advanced Cyberattack Detection in IoMT Using Convolutional Neural Networks

The proliferation of Internet of Medical Things (IoMT) has revolutionized healthcare by enhancing continuous patient monitoring, diagnostics, and treatment through the integration of interconnected medical devices. However, this transformation introduces significant cybersecurity risks, which can result in compromised patient safety and breaches of sensitive health information. The paper "Advanced Cyberattack Detection in Internet of Medical Things (IoMT) Using Convolutional Neural Networks" presents a novel approach utilizing Convolutional Neural Networks (CNNs) to enhance the detection of cyberattacks in IoMT environments.

Methodology and Dataset

The authors propose a CNN-based model, distinct from the previously employed traditional ML models, for its superior capabilities in processing temporal characteristics of network traffic data. The model is refined through a series of preprocessing, convolutional, and fully connected layers, aimed at achieving high accuracy in classifying network traffic data into benign or attack categories.

The research utilized the CICIoMT2024 dataset, which contains a comprehensive collection of network traffic data from 40 IoMT devices and encompasses 18 cyberattack types. This dataset serves as a robust foundation for training and evaluating cyberattack detection models, offering vast and varied attack scenarios.

Results

The CNN model achieved impressive results with an accuracy of 99% across binary, categorical, and multiclass classification tasks. This performance significantly surpasses that of traditional ML techniques, such as Logistic Regression, AdaBoost, and Random Forests. Specifically, while traditional methods struggled with multiclass classification tasks—often achieving accuracy rates below 73%—the proposed CNN model effectively discriminated between similar attack vectors, such as different DoS/DDoS variants.

Table 1 within the paper illustrates the superior performance metrics of the CNN model against traditional ML models. Notably, the CNN's precision, recall, and F1-scores were consistently high across various classification tasks. The comprehensive evaluation encompassed binary detection (benign vs attack), six-class classification of DDoS/DoS attacks, and an extensive 19-class categorization across all supported attack types.

Implications and Future Directions

The implications of this research are two-fold: practical and theoretical. Practically, the model's ability to deliver high accuracy in IoMT cybersecurity positions it as a superior candidate for real-world deployment in Network Intrusion Detection Systems (NIDS). This model can facilitate robust real-time threat monitoring across dynamic IoMT infrastructures, thereby mitigating threats effectively. Theoretically, the model underscores the potential of deep learning frameworks, such as CNNs, to deliver superior performance against complex, high-dimensional data typical of IoMT environments.

However, several challenges remain, such as the model's demand for computational resources, which may limit deployment on devices with limited processing capabilities. This suggests a trajectory for future research towards lightweight model architectures or the integration of edge computing to maintain efficiency across IoMT networks.

Moreover, the paper accentuates the need to probe further into attack vector-specific models or hybrid frameworks combining rule-based and behavior-based detection techniques. These future avenues could potentially enhance the robustness of cybersecurity frameworks for IoMT, adapting to the rapidly evolving threat landscape inherent in healthcare settings.

In summary, this paper offers a significant contribution to the field of IoMT cybersecurity, demonstrating the efficacy of CNNs in optimizing cyberattack detection. It opens a pathway for refined detection mechanisms that are critical for safeguarding the future of interconnected medical technologies.