Enhancing IoT Security Through Deep Learning: A Comprehensive Study

Abstract

The Internet of Things (IoT) seamlessly integrates numerous devices with minimal human intervention, enabling effective communication between them, which has further enhanced the reliability across the diverse range of applications, including health care, intelligent agriculture, home security, industrial settings, and smart cities. However, the inherent nature of IoT infrastructure and its complex deployment aspects give rise to novel security challenges. Traditional risk mitigation strategies like encryption and access control prove insufficient in detecting attacks. Therefore, it becomes imperative to enhance the current security mechanisms in order to establish a safeguarded IoT environment. The progression of deep learning techniques introduces embedded intelligence to the IoT domain, addressing various security concerns. This research paper conducts a comprehensive survey of several deep learning techniques that aimed at fortifying IoT devices against a spectrum of attacks. Furthermore, the merits and drawbacks of these methods are pondered upon, thus laying the foundation for future investigations in this area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.