Specifc Emitter Identifcation

Abstract

Accurate device identification, especially for mobile phones, is critical in today’s wireless communication security environment. Although traditional techniques such as the International Mobile Equipment Identity (IMEI) have been extensively employed for this objective, they possess weaknesses that render them vulnerable to manipulation. Consequently, new methods dubbed Specific Emitter Identification (SEI) have become more popular. With the use of cutting-edge technologies, SEI techniques identify specific emitters based on their distinct qualities, providing a more reliable means of device identification. Prior studies in SEI have investigated several approaches, each of which has added significant knowledge to the subject. These approaches include signal kurtosis, Hilbert transform, multi-channel signal analysis, time-frequency spectrum analysis, and variational mode decomposition. Building on these foundations, our research uses machine learning (ML) algorithms to further improve SEI dependability and accuracy. Through the application of machine learning techniques, our project aims to create advanced models that can more accurately identify between devices, enhancing the security of wireless networks. our project shows how ML-driven SEI techniques may be used to address the problems presented by contemporary communication security risks through empirical analysis and experimentation, opening the door to more robust and efficient identification systems. i