Enhancing Intrusion Detection with Unique RF-Driven Features

In the world of advanced technology, ensuring the security of a device or a region has become a critical concern. The proposed system aims to not only identify the devices present in the area but also detect potential intruders in real-time using a pre-established database and real time training of the model. This solution leverages advanced unique features of received signals by incorporating Software-Defined Radio (SDR) and GNU Radio for signal pre-processing, alongside machine learning algorithms for real-time model training and classification of the signals. The core focus of the system is on generating a unique "radio frequency fingerprint" for each electronic device. Since the radio frequency (RF) signal of every device is distinct, the system extracts unique features from the received signals which highlights the variations due to manufacturing hardware to create individual fingerprints. This approach forms the foundation of identifying and differentiating devices based on their RF characteristics.