RSA Security & Fraud Detection Analysis

Project Overview

This project implements an RSA cryptographic system with a strong focus on security validation, fault detection, and fraud-relevant anomaly monitoring.

Rather than treating cryptography as a black box, this project explores:

• What RSA can detect
• What RSA cannot detect
• How cryptographic failures translate into fraud signals in real systems

The project combines cryptography theory, secure implementation, and fraud detection thinking.

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Objectives

• Implement RSA encryption, decryption, and signing using large integers
• Ensure cryptographic correctness and realistic behavior
• Detect and log anomalies caused by:
  • Wrong key usage
  • Fault injection
  • Integrity mismatches
• Demonstrate why ciphertext tampering cannot be detected by RSA alone

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Key Concepts Demonstrated

✔ Cryptographic Correctness

RSA key generation (n, φ(n), e, d)

• Encryption / Decryption
• Digital signatures and verification
• Use of Python’s built-in pow() for correctness and reliability

✔ Security Engineering

• Integrity verification during decryption
• Fault injection simulation
• Detection of incorrect key usage
• Awareness of side-channel and fault-based risks

✔ Fraud Detection Perspective

• Logging suspicious events as anomalies
• Pattern accumulation from repeated faults
• Understanding detection limits (no false security claims)
• Translating cryptographic failures into fraud signals

Fraud & Anomaly Scenarios Tested

Scenario	Detected	Explanation
Ciphertext tampering	❌ No	RSA consistency holds without authentication
Repeated tampering	❌ No	Requires signatures / MACs
Wrong private key	✅ Yes	Decryption integrity fails
Fault injection	✅ Yes	Re-encryption mismatch detected

This reflects real-world fraud systems, where not all attacks are detectable.

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Test Strategy

The test suite (test_rsa.py) is structured into:

1. Basic correctness tests

   • Fraud simulation tests
   • Anomaly logging and inspection

2. Fraud simulation tests

   • Wrong key usage
   • Fault injection scenarios

3. Anomaly logging and inspection

   • Repeated abnormal behavior
   • Pattern accumulation

This mirrors how security and fraud teams validate systems in production.

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Technologies Used

• Python 3
• Large integer arithmetic
• Cryptographic modular exponentiation
• Defensive programming and anomaly logging

Project Structure

src/
├── rsa.py # Abstract RSA interface
├── rsa_impl.py # Secure RSA implementation with anomaly detection
└── test_rsa.py # Correctness + fraud simulation tests

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Background

This project originated as a university cryptography lab and was extended to reflect real-world security and fraud-detection scenarios.

Author

Mainuddin Monsur Robin