Using Deep Learning Technique in an Iot Environment to Prevent Intrusions into Internet Networks

Every day, the Internet of Things (IoT) becomes more widespread and smarter by enabling data sharing among devices. By connecting the physical and digital realms, IoT is rapidly bringing intelligence to the globe; by 2024, it is anticipated that over 20 billion items will be connected. IoT attempts to simplify our lives, yet its environment is full of security holes and potential threats. Numerous research works offer machine learning and deep learning-based intrusion detection systems for Internet of Things environments. The solutions offered differ in terms of efficiency and precision. This study proposes an RNN deep learning approach to offer an intrusion detection model in an Internet of Things context. The suggested model is trained and tested using the NSL-KDD dataset. The proposed solution has an 87% accuracy rate, which is satisfactory. We intend to apply optimization strategies in subsequent work to raise our model's detection accuracy.