READ.ME

# AI-Enhanced Examination Integrity System: Technical Architecture and Implementation

## System Overview

Our solution addresses the inadequacies of conventional remote proctoring by implementing a multi-modal anomaly detection framework that operates without traditional camera surveillance. The system employs an ensemble of machine learning algorithms to establish behavioral baselines and detect deviations during examination periods.

## Technical Architecture

### Frontend Implementation
- **Framework**: PyQt5-based UI interface with WebEngine integration
- **Data Capture**: Low-level system hooks utilizing pynput and pyWinhook for input capture
- **Instrumentation**: Event-driven architecture with asynchronous data streaming (15Hz sampling rate)

### Backend Processing
- **Core Services**: Python microservices architecture with inter-process communication via ZeroMQ
- **Parallelization**: Multi-threaded execution model with Joblib for CPU optimization (thread pooling)
- **Device Integration**: CoreBluetooth API for wireless peripheral detection and signal strength analysis

### ML/AI Implementation
- **Input Analysis**:
  - Mouse dynamics: LSTM networks (128-dimension hidden state) analyzing trajectory, velocity, and acceleration vectors
  - Keyboard dynamics: Specialized RNNs with attention mechanism for keystroke dynamics and typing cadence analysis
  - N-gram probabilistic models for sequence prediction validation

- **Screen Analysis**:
  - CNN-based object detection (YOLOv5) for unauthorized application/window identification
  - Tesseract OCR integration with NLP post-processing for textual content classification
  - Frame differential analysis with optical flow detection for rapid screen transitions

- **Risk Scoring Engine**:
  - XGBoost classifier with custom feature importance weighting
  - Isolation Forest for unsupervised anomaly detection (contamination factor: 0.03)
  - PPO reinforcement learning model for adaptive threshold adjustment

### Network Intelligence
- **Protocol Analysis**:
  - Deep packet inspection for TLS fingerprinting and proxy detection
  - DNS traffic analysis with entropy-based anomaly detection
  - TCP/IP timing analysis for latency-based virtualization detection

- **Connection Verification**:
  - MAC address validation against enrollment registry
  - BGP route analysis for geographic consistency verification
  - Split tunneling detection via simultaneous connection fingerprinting

### Posture Detection Subsystem
- **CSI Analysis**:
  - Channel State Information extraction from WiFi chipsets (Intel 9260/AX200 supported)
  - Signal processing: Butterworth filtering with 5Hz cutoff for motion isolation
  - Phase sanitization algorithm for multipath interference reduction
  - DNN-based classifier (5 convolutional layers + 3 dense layers) for posture state classification

## Technical Innovations

### Behavior Fingerprinting
Our system establishes statistically significant behavioral fingerprints during enrollment, capturing 27 distinct metrics across input modalities. Principal Component Analysis reduces dimensionality while preserving 97.3% of variance, enabling efficient real-time comparison.

### Adaptive Risk Assessment
The system implements a Bayesian risk framework that dynamically adjusts sensitivity based on:
- Historical user behavior patterns (weighted temporal importance)
- Examination complexity factors (autoregressive difficulty mapping)
- Environmental context variables (signal/noise analysis)

### Privacy-Preserving Architecture
- Zero data persistence beyond examination sessions
- Federated model training with differential privacy (ε=3.6)
- Homomorphic encryption for sensitive metric comparison

## Current Development Status
- Completed mouse/keyboard behavioral models (F1 score: 0.92)
- Screen monitoring subsystem operational (precision: 0.87, recall: 0.91)
- Risk scoring framework validated against synthetic adversarial attacks
- CSI-based posture detection achieves 89.7% classification accuracy

Further optimization of the neural architecture is underway to reduce computational overhead, alongside implementation of transfer learning techniques to accelerate user-specific model adaptation.