With the rapid increase in connected devices and SoC design architecture being usedin diverse platforms, they become potential targets to gain unauthorized access for data and privacy invasion. Therefore, heterogeneous SoC architecture raises security concerns in addition to the benefits they offer with improved throughput. They are susceptible to side-channel attacks where secure information is extracted through communication channels. Crypto algorithms implemented for secure authentication tend to leak sensitive information jeopardizing system security. Memory corruption vulnerabilities, code injection, buffer overflow attacks and other software-based attacks through untrusted channels tend to control the flow of the application with malicious data. Though traditional defense mechanisms have been implemented they are all still vulnerable to side-channel attacks. Secure measures to protect the interfaces and data propagation through different channels are critical and building a resilient model consists of the on-chip security factors. In this work, a platform based SoC model is implemented to meet the security objectives using the RISC-V architecture in which an information flow tracking module tracks the flow of data for the system’s integrity along with crypto engines and a secure boot mechanism for secure device authentication providing encrypted data transfers. For bitstream resilient SoC models the work extends a logic obfuscation module with runtime security leading to a secure assessment framework. This work explores the microarchitectural vulnerabilities with Machine Learning models and proposes a Transfer Learning technique based counterfeit detection scheme for supply chain vulnerability.