Power Analysis Based Side Channel Attacks (SCAs) on FPGA

Abstract

In today’s interconnected world, a vast number of computer systems are globally connected, creating a global village. These systems are remotely accessible and share a significant amount of data on the cloud, raising concerns about data and system security. Field Programmable Gate Array (FPGA) technology has gained popularity due to its fast computation, reconfigurability, and post-manufacturing reprogrammability. FPGAs are built on current semiconductor technologies, making them susceptible to disturbances from alterations in their fabrication process and runtime conditions. These variations can have security implications that are not extensively explored. In our research project, we investigated potential security issues related to side-channel attacks (SCAs) on FPGAs and explored possible countermeasures. Firstly, we focused on power analysis or power profiling of FPGAs, which rely on measuring voltage fluctuations during encryption tasks. These voltage fluctuations in the cryptographic module can be measured using physical sources like an oscilloscope or remote sources like delay line sensors. Secondly, we delved into power analysis-based SCAs that leverage these voltage measurements to extract the secret key. Thirdly, we devised a framework based on machine learning and deep learning algorithms to predict secret keys and execute successful attacks. Our custom CNN model outperformed previous studies, achieving a significant improvement of approximately 46% by successfully attacking with only 570 attack power traces. Fourthly, we explored state-of-the-art resilient countermeasures against power analysis-based SCAs on FPGAs and identified the hiding technique as the most effective one. Looking ahead, these attacks are not limited to individual FPGAs. Cloud FPGAs and IoT devices are also susceptible to power analysis attacks, exploiting partial or full access to the power distribution networks (PDN). Therefore, addressing these security concerns is crucial for ensuring the safety and integrity of FPGA-based systems and IoT devices in the future.