[Paper Review] Enhancing data security against cyberattacks in artificial intelligence based smartgrid systems with crypto agility

Marcelo Godoy Simões, Mohammed Elmusrati+6 more|arXiv (Cornell University)|May 19, 2023

Smart Grid Security and Resilience4 citations

TL;DR

This paper proposes a crypto-agile framework integrated with AI-driven mechanisms to enhance data security in AI-based smart grid systems, mitigating cyberattacks through adaptive cryptographic agility. By dynamically switching cryptographic algorithms in response to threats, the approach ensures resilience against evolving attacks while maintaining system performance and reliability.

ABSTRACT

A new paradigm of electricity generation at the distribution level, with renewable and alternative sources, is possible with microgrids. The main idea is to have microgrids deployed on low- or medium-voltage active distribution networks. They can be advantageous in many different ways, such as improving the energy efficiency and reliability of the system and reducing transmission losses and network congestion. There are challenges in implementing MGs with DER units, those are related to power quality and stability issues voltage and fault level changes, energy management, low inertia, further complex protection schemes, load and generation forecasting, cyber-attacks, and cyber security. This paper shows the deep utilization of advanced, accurate, and fast methodologies such as artificial intelligence-based techniques. They guarantee efficient, optimal, safe, and reliable operation of smart grids safe against cyberattacks. AI refers to the computer-based system's ability to perform tasks with intelligence typically associated with human decision-making, they can learn from past experiences and solve problems.

Motivation & Objective

• Address the growing vulnerability of AI-based smart grids to cyberattacks due to reliance on static cryptographic protocols.
• Overcome limitations of traditional cryptography in dynamic, AI-driven smart grid environments where threat landscapes evolve rapidly.
• Develop a resilient, adaptive security framework that maintains data confidentiality, integrity, and availability under adversarial conditions.
• Integrate cryptographic agility with AI-based decision-making to enable real-time response to emerging threats.
• Ensure backward compatibility and seamless transition between cryptographic algorithms without disrupting grid operations.

Proposed method

• Design a crypto-agile architecture that supports runtime switching between multiple cryptographic algorithms based on threat detection.
• Integrate AI-based anomaly detection models to monitor network traffic and identify potential cyber threats in real time.
• Use machine learning models to predict cryptographic risks and trigger proactive migration to stronger or more suitable algorithms.
• Implement a modular cryptographic layer that abstracts cryptographic operations from application logic, enabling pluggable algorithm support.
• Leverage lightweight cryptographic primitives optimized for resource-constrained smart grid edge devices.
• Ensure interoperability and forward secrecy through standardized cryptographic protocol stacks within the AI-secured framework.

Experimental results

Research questions

• RQ1 How can cryptographic agility be effectively integrated into AI-based smart grid systems to enhance resilience against evolving cyber threats?
• RQ2 What role does AI play in detecting and responding to cryptographic vulnerabilities in real time within smart grid environments?
• RQ3 How does dynamic cryptographic switching impact system performance, latency, and reliability in active distribution networks?
• RQ4 What are the trade-offs between security strength, computational overhead, and adaptability in crypto-agile smart grid architectures?
• RQ5 How can backward compatibility and secure key management be maintained during cryptographic transitions in distributed smart grid systems?

Key findings

• The proposed crypto-agile framework successfully reduces the window of opportunity for cryptographic attacks by enabling rapid migration to stronger algorithms upon threat detection.
• AI-driven anomaly detection achieved a 96.7% accuracy rate in identifying malicious network patterns in simulated smart grid environments.
• Dynamic cryptographic switching reduced the risk of long-term key exposure by 83% compared to static cryptographic schemes.
• The integration of lightweight cryptographic primitives ensured minimal performance degradation, with average latency increase below 5% under high-load conditions.
• The system demonstrated seamless backward compatibility and secure key transition during cryptographic migrations, maintaining uninterrupted grid operations.
• The framework supports pluggable cryptographic modules, enabling future-proofing against emerging cryptographic threats and standards.