DeepSecIoT: An Advanced Deep Learning-Based Algorithm for Enhancing Security in Wireless IoT Devices

Abstract

The proliferation of wireless Internet of Things (IoT) devices has ushered in a new era of connectivity and automation, revolutionizing various industries and aspects of daily life. However, this exponential growth has also exposed vulnerabilities that demand robust security solutions. In response to these challenges, this research introduces DeepSecIoT, an innovative deep learning-based algorithm meticulously engineered to enhance the security of wireless IoT devices. DeepSecIoT leverages the power of deep neural networks and signal processing techniques to adapt to a spectrum of signal conditions, including varying signal strengths, modulation schemes, and noise levels. Through rigorous experimentation, this study assesses DeepSecIoT's versatility and reliability in securing IoT ecosystems. The results demonstrate the algorithm's efficacy under diverse scenarios, including strong signal conditions and various modulation schemes. Additionally, the paper discusses insights gained, strengths observed, and potential areas for improvement. DeepSecIoT's potential to fortify IoT security is highlighted, along with its significance in an ever-connected world. This research lays the foundation for continued advancements in IoT security, with DeepSecIoT at the forefront.