All major vendors currently offer Remote Keyless Entry (RKE) systems for passenger vehicles, which have become a standard feature of modern cars. These systems enable wireless communication between drivers and vehicles, facilitating convenient functions such as remote start and passive keyless entry. Unfortunately, the existing security measures implemented in these systems are often inadequate, leaving them vulnerable to intrusions and a wide array of attacks. To address these vulnerabilities, we present SecFob, a solution that combines quantum random number generation (QRNG), machine learning, and encryption techniques. SecFob is designed to defend against common attack techniques by leveraging the perfect entropy offered by QRNG and by incorporating machine learning techniques, specifically utilizing the Siamese Neural Network. These solutions are cost-effective, portable, and with the use of ML technique, are able to detect amplified signals.