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Secure Your Hardware with Randomization and Redundancy
No full textDifferential Fault Analysis (DFA) is a potent hardware attack that threatens cryptographic security by injecting faults into a cipher implementation to reveal secret keys. This project aims to mitigate DFA attacks on the Advanced Encryption Standard (AES) by implementing targeted countermeasures in an embedded AES-128 encryption core. Two key techniques are explored: Randomization and Triple Modular Redundancy (TMR). The randomization approach introduces unpredictability into the encryption process, which involves inserting dummy rounds, artificial noise, and random delays, to disrupt an attacker’s timing and analysis, while TMR provides redundancy by replicating critical rounds of computation and using majority voting to correct any single-fault errors. The effectiveness of these countermeasures was evaluated using a real fault-injection attack scenario: a clock glitch was used to induce faults in the AES encryption process, and the outputs were analyzed for key leakage. Results show that with the countermeasures in place, the DFA attack was unable to recover the AES secret key, whereas an unprotected AES implementation succumbed to key extraction. These findings highlight the importance of integrating hardware-level defenses to secure cryptographic devices against fault injection attacks, achieving improved security with minimal performance trade-offs
Vintage Game Emulator
No full textThis project explores the integration of artificial intelligence into retro-style arcade titles to enhance the gameplay, while still preserving the nostalgic aesthetic and overall mechanics of the titles. By using Pygame to explore clones of the existing video game titles, we modified the existing games to develop AI into the games using various AI algorithms. The system introduces adaptive enemy behavior that responds to the players actions and also introduces an additional player to some games that will play alongside the player as well. This helps to create a more engaging and unpredictable gaming experience. The AI logic is implemented in python within the player class itself for the games with additional players.
The primary goal of the project is to explore various AI algorithms to maintain an understanding of how AI can be implemented into various arcade titles effectively. By doing this it allows us to select the best algorithms for this project that can help enhance a player’s experience, instead of frustrating the player. The result of our project demonstrates the potential for AI to bring new life to these older video games, which allows us as the developers to discover how we can effectively and creatively reinterpret the familiar gameplay
WeSearch - SCU’s Research Hub
No full textSanta Clara University (SCU) hosts a flourishing undergraduate research program, but discovering research opportunities remains inefficient due to inconsistent information channels. Students rely on informal communication and disconnected departmental web pages to find research roles. In response, our senior design team developedWeSearch: a centralized, web-based platform that consolidates research opportunities across all SCU laboratories. It allows students to browse labs, projects, and apply directly to open positions. Faculty members can create lab profiles, post research openings, manage applications, and assign administrative roles to student assistants.
The platform was designed using Figma and implemented with React.js on the front-end. Firebase was used for real-time data storage, with Google OAuth providing secure user authentication. We integrated Elasticsearch via a Node.js backend to deliver efficient search capabilities. The system was containerized using Docker and deployed through a continuous integration/continuous development (CI/CD) pipeline to SCU’s Google Cloud Platform (GCP) infrastructure, ensuring scalability and maintainability
Drop Ceiling Inspection Robot
No full textElectricians face significant health and safety hazards when inspecting drop ceilings, including exposure to dust, asbestos, and the risk of falls. To address these challenges, this project proposes a lightweight, autonomous robot capable of inspecting drop ceilings and assisting with wire tracing tasks—thereby distancing electricians from hazardous environments. The robot employs tread-based mobility to navigate fragile ceiling panels, integrated ultrasonic sensors and bumpers for obstacle avoidance, and an antenna system to detect and follow energized wire signals. Visual feedback is provided to the operator through a real-time video feed over a secure NoMachine interface, with manual and semi-autonomous operation modes supported. The system incorporates both hardware-based and digital signal filtering to isolate desired wire signals. Testing in a simulated drop ceiling environment demonstrated the robot’s ability to traverse obstacles, detect target signals, and operate effectively in low-light conditions. This solution has the potential to enhance workplace safety and efficiency for electricians performing drop ceiling inspections