Enhance 3DES Smartcard Deep Learning SCA Pipeline

# Project Requirement: Deep Learning Side-Channel Analysis Pipeline for 3DES Smartcards

## Project Overview

We are seeking an experienced security engineer / machine learning specialist to update and correct an existing Deep Learning-based Side-Channel Analysis (SCA) pipeline for Triple-DES (3DES) smartcards. The current pipeline successfully recovers session keys from known-input traces but produces incorrect 3DES keys when applied to blind forensic traces, despite achieving 100% attack success rate.

## Background

The existing pipeline implements a hypothesis-based profiling attack using Multi-Layer Perceptrons (MLP) to extract 3DES session keys (K_ENC, K_MAC, K_DEK) from electromagnetic traces. While the system achieves 100% recovery on standard datasets (Mastercard/Visa with known inputs), it fails to derive correct 3DES keys on blind traces (GreenVisa dataset) where input logs are corrupted or missing.

## Technical Requirements

### Current Implementation Details

- **Target Algorithm**: Triple-DES in Outer CBC mode
- **Attack Model**: Per S-box identity leakage model (16-class classification)
- **Architecture**: 24 independent MLP models (8 per key component)
- **Network Structure**:
- Input: 100 Points of Interest (POI)
- Hidden Layer 1: 400 neurons + ReLU + Batch Normalization
- Hidden Layer 2: 200 neurons + ReLU + Dropout (0.3)
- Output: 16 neurons + Softmax
- **Optimizer**: Adam (learning rate = 1e-3)
- **Loss Function**: Categorical Crossentropy
- **Pre-processing**: Z-score normalization, Sum of Absolute Differences feature selection

### Current Issue

The pipeline achieves:
- Training accuracy: >99.2%
- Attack success (known inputs): 100% with single trace
- Attack success (blind traces): 100% with single trace (using hypothesis solver)

**However, the recovered 3DES keys for blind traces are cryptographically invalid/incorrect**, despite satisfying internal consistency checks (parity bits, weak-key detection, structural constraints).

## Required Updates

### 1. **Root Cause Analysis**
- Investigate why recovered keys from blind traces are incorrect despite 100% attack success
- Analyze hypothesis solver's determination of null vector as true input
- Validate S-box collision resolution logic (currently reduces search from 2^48 to 2^8)

### 2. **Pipeline Corrections**
- Fix key derivation logic to ensure cryptographically valid 3DES keys
- Validate key expansion and parity bit handling according to FIPS 46-3
- Implement additional cryptographic consistency checks
- Verify PC-1 and PC-2 permutation operations

### 3. **Blind Input Solver Enhancement**
- Improve hypothesis space exploration beyond null vector assumption
- Implement multiple hypothesis validation with cross-validation
- Add statistical confidence metrics for hypothesis selection
- Consider alternative input values based on APDU context

### 4. **Validation Framework**
- Implement automated cryptographic validation of recovered keys
- Add test vectors for known-key verification
- Create regression test suite with synthetic blind traces
- Develop metrics for key correctness beyond attack success rate

### 5. **Documentation Requirements**
- Document corrected key derivation process
- Provide analysis of why original pipeline failed on blind traces
- Include validation procedures and test results
- Update architecture diagrams and data flow

## Deliverables

1. **Updated Source Code**
- Corrected Python implementation of the complete pipeline
- All dependencies and requirements specified
- Configuration files for both known-input and blind scenarios

2. **Test Suite**
- Unit tests for cryptographic operations
- Integration tests on Mastercard/Visa dataset
- Validation tests on GreenVisa blind dataset
- Performance benchmarks

3. **Technical Documentation**
- Detailed explanation of corrections made
- Analysis of root cause
- User guide for running pipeline on new datasets
- API documentation

4. **Validation Report**
- Demonstration of correct key recovery on blind traces
- Comparison with original pipeline results
- Success metrics and confidence intervals

## Qualifications

### Required Skills
- Strong background in side-channel analysis and cryptographic implementations
- Experience with deep learning for security applications (DL-SCA)
- Proficiency in Python and deep learning frameworks (TensorFlow/Keras or PyTorch)
- Understanding of DES/3DES algorithm and key schedule
- Experience with electromagnetic/power trace analysis

### Preferred Qualifications
- Prior work on template attacks or profiling SCA
- Knowledge of smartcard protocols (APDU, ISO/IEC 7816)
- Experience with forensic analysis of secure elements
- Publications in CHES, TCHES, or similar venues

## Application Requirements

Interested freelancers should submit:
1. CV highlighting relevant SCA/DL experience
2. Examples of previous work on cryptographic implementations or side-channel analysis
3. Proposed approach and timeline
4. Estimated budget

## Additional Information

The full dataset (Mastercard profiling, GreenVisa target traces) and current codebase will be provided to the selected candidate. NDA required due to sensitive nature of forensic data.


*Note: This project requires understanding of both cryptographic implementations and modern deep learning techniques. Please only apply if you have demonstrable experience in both domains.*