The entire computing paradigm is changed due to the technological advancements in Information and Communication Technology (ICT). Due to these advancements, various new communication channels are being introduced, out of which the Internet of Things (IoT) plays a significant role. The Internet of Medical Things (IoMT) is a special category of IoT in which the medical devices communicate with each other for sharing sensitive data. These advancements help the healthcare industry to have better contact and care towards their patients. But they too have certain drawbacks since there are so many security and privacy issues like replay, man-in-the-middle, impersonation, privileged-insider, remote hijacking, password guessing, denial of service (DoS) attacks and malware attacks. When the sensitive data is being attacked by any of these attacks, there is a chance of losing the authorized data to the attacker or getting altered due to which the data is not available for the authorized users and customers. Machine learning algorithms are widely used in the Intrusion Detection System (IDS) for detecting and classifying the attacks at the network and host level in a dynamic manner. Many supervised and unsupervised algorithms have been designed by researchers from the area of machine learning and data mining to identify the reliable detection of an anomaly. However, the main challenge in the IDS models are changed in dynamic and random behavior of malicious attacks and designing a scalable solution that can handle this behavior. The rapid change in network behavior and the fast evolution of various attacks paved the way for evaluating various datasets that are generated over the years and to design different dynamic approaches. In this paper, a deep neural network (DNN) is used to develop effective and efficient IDS in the IoMT environment to classify and predict unforeseen cyberattacks. The network parameter are preprocessed, optimized and tuned by hyperparameter selection methods. A comprehensive analysis of experiments in DNN with other machine learning algorithms are compared on the benchmark intrusion detection dataset. Through rigorous testing, it has proved that the proposed DNN model performs better than the existing machine learning approaches with an increase in accuracy by 15% and decreases in time complexity by 32%, which helps in faster alerts to avoid post effects of intrusion in sensitive cloud data storage.

整个计算范例因信息和通信技术（ICT）的技术进步而改变。由于这些进步，引入了各种新的通信渠道，其中物联网（IoT）发挥了重要作用。医疗物联网（IoMT）是IoT的特殊类别，其中医疗设备相互通信以共享敏感数据。这些进步有助于医疗保健行业更好地与患者接触和护理。但是它们也有某些缺点，因为存在许多安全和隐私问题，例如重播，中间人，模拟，特权内幕人士，远程劫持，密码猜测，拒绝服务（DoS）攻击和恶意软件攻击。当敏感数据受到上述任何攻击时，可能会丢失授权数据给攻击者或对其进行更改，从而导致授权用户和客户无法使用该数据。入侵检测系统（IDS）中广泛使用了机器学习算法，以动态方式在网络和主机级别检测和分类攻击。研究人员从机器学习和数据挖掘领域设计了许多有监督和无监督算法，以识别异常的可靠检测。但是，IDS模型的主要挑战在于恶意攻击的动态和随机行为发生变化，并设计了可处理这种行为的可伸缩解决方案。网络行为的迅速变化和各种攻击的快速发展为评估多年来生成的各种数据集并设计不同的动态方法铺平了道路。在本文中，深度神经网络（DNN）用于在IoMT环境中开发有效的IDS，以对无法预料的网络攻击进行分类和预测。网络参数通过超参数选择方法进行预处理，优化和调整。在基准入侵检测数据集中比较了DNN与其他机器学习算法的实验的综合分析。通过严格的测试，证明了所提出的DNN模型的性能优于现有的机器学习方法，其准确性提高了15％，时间复杂度降低了32％，