GAM: Introducing a Novel Algorithm for Advanced Ultralightweight Cryptography for IoT devices
Year: 2025
Type: article
Abstract: This paper introduces the Generic Algorithmic Security (GAM) algorithm, a groundbreaking advancement in ultralightweight cryptography tailored for the quantum computing era. As quantum computing chal-lenges traditional cryptographic methods, GAM emerges as a robust and adaptive solution. It employs innovative features such as Self-Healing Cryptographic Keys (SHCK) and Energy-Aware Dynamic En-cryption Scaling (EDES), ensuring resilience and optimising encryption based on real-time energy and computational constraints. Our study reveals that GAM outperforms existing algorithms like SIMON and SPECK in speed, energy efficiency, and update flexibility. Designed for resource-constrained environments like IoT devices and wearable technology, GAM offers quantum-proof security while maintaining opera-tional efficiency. We delve into its technical architecture, detailing its encryption and decryption processes, and conduct a thorough security analysis showcasing GAM's exceptional adaptability to current and future cyber threats. The results demonstrate GAM’s superior performance, achieving a 20% increase in encryp-tion speed and a 15% improvement in energy efficiency compared to SIMON and SPECK. Additionally, GAM’s update flexibility scored 30% higher, indicating its proactive adaptability to evolving threats. These findings highlight GAM’s potential as a future-proof solution, providing robust protection and operational efficiency in diverse digital environments. GAM represents a significant leap forward in cryptography, offering a future-proof solution to safeguard digital information against the challenges posed by quantum computing, underscoring the importance of innovative cryptographic solutions in securing tomorrow's digital landscape.
Author: Ghada Al-Kateb
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412362187