GYOZ S-DDS

Inspiration

Traditional cybersecurity relies heavily on static credentials like passwords or basic biometrics, which fail to detect if an authorized user is operating under physical or psychological duress. We wanted to build a cognitive biometric defense system capable of silently protecting critical institutional assets.

What it does

GYOZ S-DDS is a multi-modal cognitive defense architecture:

• EEG Brainwave Authentication: Verifies identity by measuring unique neurological response patterns when a user views pre-selected visual stimuli.
• Silent Duress Alerting: Detects physiological stress or unauthorized access attempts, quietly routing emergency alerts to central security without triggering visible alarms.
• Multi-Factor Hardware Security: Integrates secondary biometric verification directly into system hardware controls (mouse/keyboard sensors).
• DNA-Inspired Storage Architecture: Utilizes ultra-dense encoding concepts to store biometric template data with minimal footprint.

How we built it

I designed the system logic using C++ and Python algorithms to process biometric pattern recognition and signal parameters, alongside custom workflow diagrams for peripheral hardware simulation

Challenges we ran into

Building an adaptive system that accurately distinguishes between standard emotional responses and actual duress required careful threshold mapping for BCI signal patterns

Accomplishments that we're proud of

Successfully designing a multi-layered cognitive security framework that combines real-time EEG brainwave matching with dynamic duress detection. Formulating a silent alert protocol that protects users under physical coercion without exposing system state on the primary screen. Structuring a ultra-dense biometric data architecture inspired by DNA storage principles to reduce memory overhead.

What we learned

Deepened my knowledge in Brain-Computer Interface (BCI) signal patterns and biometric authentication metrics. Gained hands-on experience in structuring threat-detection algorithms using C++ and Python. Mastered the integration of secondary hardware verification concepts (such as biometric peripherals) alongside primary software encryption.

What's next for GYOZ S-DDS

Transitioning from simulated algorithms to full sandbox deployment and testing secondary biometric sensor integrations.

Built With

• bci
• biometrics
• c++
• cybersecurity
• machine-learning
• python