Offline Labs

This repository contains two standalone offline lab assignments:

1. Lab 1: Cryptography Implementation (Offline1)
2. Lab 2: Website Fingerprinting Pipeline (Offline2)

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Lab 1: Cryptography Implementation (Offline1)

Implement core cryptographic primitives and a secure client–server exchange.

Directory: Offline1/

• 2005018_task1.py
  • AES key schedule (key expansion) for 128/192/256-bit keys
  • PKCS#7 padding/unpadding
  • Byte ↔ Word conversions, SubWord, RotWord, MixColumns, AddRoundKey helper functions.
• 2005018_task2.py
  • Elliptic curve setup over prime fields
  • Modular inverse, Tonelli–Shanks square root
  • Point addition, doubling, and scalar multiplication
• 2005018_task3_ALICE.py & 2005018_task3_BOB.py
  • Socket-based ECDH key exchange (Alice ↔ Bob)
  • Derive shared secret → SHA-256 → AES-CBC encrypt/decrypt a test message
• 2005018_utils.py
  • Shared S-boxes, inverse S-boxes, Mixers, Rcon constants, BitVector utilities

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Lab 2: Website Fingerprinting Pipeline (Offline2)

Starter code for collecting, storing, post-processing, and classifying network timing traces.

Directory: Offline2/starter_code/template/

• app.py
  • Flask server skeleton
  • Endpoints:
    • /collect_trace (POST) – receive JSON traces, generate heatmaps
    • /api/clear_results (POST) – reset stored traces/heatmaps
• collect.py
  • Selenium-based trace collector
  • Automates opening fingerprinting page, interacting with target sites, retrieving results
• database.py
  • SQLAlchemy models: Fingerprint, CollectionStats
  • Methods to init DB, save traces, query counts, export JSON
• PostProcess.py
  • Re-index dataset entries
  • Write out corrected JSON for training
• train.py
  • PyTorch pipeline with two models:
    • FingerprintClassifier (simple CNN)
    • ComplexFingerprintClassifier (deeper CNN + batch norm)
  • Training loop, evaluation scaffold, classification report
• static/index.html
  • Pico CSS + Alpine.js UI
  • Buttons for “Collect Trace”, “Download Traces”, “Clear Results”
  • Table for latency results and heatmap gallery

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Setup & Usage

Note: We recommend creating a Python virtual environment for Lab 1 to isolate dependencies.

1. Lab 1 (Cryptography)

   cd Offline1
   # Create & activate a venv
   python3 -m venv venv
   source venv/bin/activate        # Linux/macOS
   # OR on Windows:
   # .\venv\Scripts\activate
   
   # Install core dependencies
   pip install sympy bitvector
   
   # Run the AES & ECC tasks:
   python3 2005018_task1.py
   python3 2005018_task2.py
   
   # In separate terminals, run Alice & Bob for ECDH:
   python3 2005018_task3_BOB.py
   python3 2005018_task3_ALICE.py
   
   

2. Lab 2 (Website fingerprinting)

cd Offline2/starter_code/template

# Create & activate a venv
python3 -m venv venv
source venv/bin/activate        # Linux/macOS
# OR on Windows:
# .\venv\Scripts\activate

# Install dependencies
pip install Flask selenium sqlalchemy torch torchvision matplotlib

# Launch backend server
python app.py

# In another terminal:
python collect.py

# train models
python train.py