AI-based anomaly detection on RISC-V. This thesis aims at creating a powerful profiling infrastructure based on the latest specifications of the RISC-V HPM on a selected RISC-V core in order to provide PMCs in the architecture. The presence of PMCs provide aggregate information (e.g., number of memory transactions) that are useful to profile applications at run-time introducing a negligible overhead. The new profiling infrastructure provides to analyze softer and identify malicious modifications. However, the complexity of modern software still represents a challenge. Traditional tools for software verification and validation present limitations that may impact the quality of software. This thesis focuses on applying AI to support software developers in anomaly detection activities.