XOR-PUF Security Analysis for Operational Technology (OT) Systems

Introduction

This project focuses on enhancing cybersecurity in Operational Technology (OT) systems by analyzing and predicting XOR-based Physical Unclonable Functions (PUFs) using Support Vector Machines (SVMs). By understanding XOR-PUF behavior, we aim to improve the security of OT systems against potential threats.

Objectives

• Develop SVM-based Solvers: Create solvers from scratch to model XOR-PUF behavior.
• Advanced Feature Mapping: Implement techniques to capture XOR-PUF complexities.
• Performance Evaluation: Test the model with extensive datasets.
• Cybersecurity Focus: Enhance security in OT systems.

Methodology

Data Generation:

• 10,000 training and 20,000 testing challenge-response pairs.
• Simulated XOR-PUFs with varying complexities.

Model Development:

• SVM-based solvers using primal gradient descent and MBSGD.
• Advanced feature mapping to capture XOR-PUF behavior.

Performance Evaluation:

• Achieved 99% accuracy in predicting XOR-PUF responses.
• Evaluated performance in complex OT environments.
• Cybersecurity Implications for OT Systems
• Enhancing Authentication Mechanisms
• Secure device authentication to prevent spoofing and cloning attacks.
• Strengthening Key Management
• Use unique challenge-response pairs for secure key management.
• Mitigating Tampering and Cloning Risks
• Detect anomalies and mitigate risks of tampering and unauthorized replication.
• Future Work and Security Recommendations
• Scalability Testing: Extend analysis to larger datasets and more complex PUFs.
• Integration with Security Protocols: Integrate XOR-PUF solutions with existing OT security protocols.
• Real-world Deployment: Pilot XOR-PUF-based security in live OT environments.