| Proj No. | A2275-251 |
| Title | Machine Learning-Based Side Channel Power Analysis |
| Summary | Hardware security is becoming increasingly critical as more devices are interconnected in the Internet of Things (IoT) era. This project aims to develop a machine learning-based approach to perform cryptographic key recovery through side-channel analysis. By leveraging the unique signatures present in electromagnetic (EM) emissions during cryptographic operations, the project seeks to train machine learning models capable of efficiently recovering cryptographic keys from targeted devices. In this project, the student needs to set up an experimental environment for capturing EM signals generated by targeted cryptographic devices. The student also needs to develop and train machine learning models capable of predicting cryptographic keys. Proficiency in the Python language is required, along with a strong understanding of machine learning techniques and frameworks (e.g., TensorFlow, PyTorch). |
| Supervisor | A/P Gwee Bah Hwee (Loc:S1 > S1 B1B > S1 B1B 42, Ext: +65 67906861) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | IC DESIGN II (Loc: S1-B2B-10) |
| Single/Group: | Single |
| Area: | Smart Electronics and IC design |
| ISP/RI/SMP/SCP?: |