Improved Cybersecurity for Healthcare Internet of Things (IoT) Devices and Wearables with the Use of State-of-the-Art Deep Learning Techniques: Strategies for Threat Detection and Data Protection

Abstract

Continuous monitoring, individualized therapies, and efficient data collecting are just a few ways in which the proliferation of wearable electronics and Internet of Things (IoT) devices has revolutionized healthcare. New cybersecurity threats, such as exposure to hackers, data breaches, and cyberattacks, are introduced with these innovations. Strong cybersecurity safeguards for IoT devices are critical, especially considering the sensitive nature of healthcare data. The goal of this project is to improve the security of healthcare IoT systems by detecting threats and effectively protecting sensitive data using state-of-the-art deep learning algorithms. In order to identify irregularities and categorize cyber dangers in real-time, the suggested system incorporates deep learning models such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). By spotting changes from typical device behavior, these models enable early detection of harmful behaviors like malware and distributed denial-of-service (DDoS) assaults. Even in IoT settings with limited resources, important healthcare data is protected by incorporating deep learning-enhanced encryption algorithms to safeguard data transmission. This research makes a significant advancement by utilizing federated learning. This method allows for various IoT devices to work together in model training without directly exchanging private data. As a result, patient privacy is preserved and system security is improved. Deep learning-based techniques outperform conventional methods in terms of threat detection accuracy and data security when tested on real-world healthcare IoT datasets. These results highlight the need for more sophisticated deep learning methods to protect healthcare IoT devices from potential cyber threats.