Khan Maryam Mahsal, Alkhathami Mohammed
Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, 25000, Pakistan.
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia.
Sci Rep. 2024 Mar 11;14(1):5872. doi: 10.1038/s41598-024-56126-x.
Internet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation. At the server, different machine learning algorithms are applied which help in early diagnosis of diseases and issue alerts in case vital signs are out of the normal range. Different cyber attacks can be launched on IoT devices which can result in compromised security and privacy of applications such as health care. In this paper, we utilize the publicly available Canadian Institute for Cybersecurity (CIC) IoT dataset to model machine learning techniques for efficient detection of anomalous network traffic. The dataset consists of 33 types of IoT attacks which are divided into 7 main categories. In the current study, the dataset is pre-processed, and a balanced representation of classes is used in generating a non-biased supervised (Random Forest, Adaptive Boosting, Logistic Regression, Perceptron, Deep Neural Network) machine learning models. These models are analyzed further by eliminating highly correlated features, reducing dimensionality, minimizing overfitting, and speeding up training times. Random Forest was found to perform optimally across binary and multiclass classification of IoT Attacks with an approximate accuracy of 99.55% under both reduced and all feature space. This improvement was complimented by a reduction in computational response time which is essential for real-time attack detection and response.
物联网（IoT）在医疗保健领域的集成改善了患者护理，同时也使医疗保健交付系统更有效、更经济。为了充分实现物联网在医疗保健中的优势，必须克服数据安全、互操作性和伦理考量等问题。物联网传感器定期测量患者的健康相关数据，并将其共享给服务器进行进一步评估。在服务器端，应用不同的机器学习算法，有助于疾病的早期诊断，并在生命体征超出正常范围时发出警报。可以对物联网设备发动不同的网络攻击，这可能导致医疗保健等应用的安全和隐私受到损害。在本文中，我们利用公开可用的加拿大网络安全研究所（CIC）物联网数据集来建模机器学习技术，以高效检测异常网络流量。该数据集包含33种物联网攻击类型，分为7个主要类别。在当前研究中，对数据集进行了预处理，并在生成无偏差的监督（随机森林、自适应增强、逻辑回归、感知机、深度神经网络）机器学习模型时使用了平衡的类表示。通过消除高度相关的特征、降低维度、最小化过拟合和加快训练时间，对这些模型进行了进一步分析。结果发现，随机森林在物联网攻击的二分类和多分类中表现最佳，在减少特征空间和全特征空间下的准确率约为99.55%。这种改进伴随着计算响应时间的减少，这对于实时攻击检测和响应至关重要。
