Fault Detection in Smart Cards

Abstract

Smart cards are ubiquitous in industries as varied as finance, communication, secure access, and identification, demanding strong fault detection systems to safeguard the integrity and security of digital transactions. This project offers an exhaustive examination and innovation of progressive fault detection strategies geared towards smart card technology. We assess the existing state of fault detection, pinpointing key obstacles such as restricted processing capabilities, advanced security threats, and the imperative to balance affordability with robust security. Utilizing an assortment of techniques, from error detection codes and hardware safeguards to side-channel scrutiny and the burgeoning areas of machine learning and AI, we introduce a nuanced framework aimed at improving the identification and mitigation of faults in smart cards. Our method integrates a simulated attack environment with a live anomaly detection system, employing flexible machine learning models that evolve through insights gained from transaction data to foresee and counteract faults. Furthermore, the project investigates the financial dimensions by adopting a cost-security optimization strategy, affirming the feasibility of our solutions while upholding the stringent security standards necessary for sensitive operations