Security Analysis of Conventional Attack by Suitable RFID Based Deep Learning Method in Industrial IoT

Radio Frequency Identification (RFID) is a technology for tracking and identifying objects, creatures or people even in a harsh environment. This technology makes use of a electromagnetic field generated by an RFID reader to identify and locate tags attached to objects. With its advantages such as long-range sensing, high mobility, and overall cost effectiveness, RFID has been widely adopted by the industrial Internet of Things (IoT) industry. Recently, RFID based deep learning methods are emerging as promising solutions for industrial IoT applications. Deep learning, a type of artificial intelligence, uses large datasets to accurately and quickly identify objects and patterns. By combining this technology with RFID, industrial IoT applications can address challenges such as efficient resource management, production optimization, quality assurance and maintenance. RFID based deep learning methods have already been implemented in various industries such as manufacturing and logistics, wherein it efficiently tracks and monitors resources, processes, and personnel. With the advancement of this technology, future industrial IoT applications can offer greater efficiency, scalability, and reliability.