Secure and Scalable Machine-to-Machine Secrets Management Solutions

Abstract

Machine-to-Machine (M2M) communication is integral to the Internet of Things (IoT) landscape. However, the increasing reliance on M2M networks introduces significant security challenges, particularly in managing secrets like credentials, API keys, and cryptographic keys. This paper explores the evolution of secure and scalable M2M secrets management solutions, offering insights into design principles, frameworks, and practical implementations. A systematic review of the literature (2003–2021) underpins the discussion, focusing on key management schemes, secure storage, and distributed systems. The study emphasizes emerging trends such as hardware security modules (HSMs), blockchain-based solutions, and zero-trust architectures. Practical implications, case studies, and a roadmap for future research are also discussed to provide a comprehensive understanding of how secrets management can evolve to address the dynamic requirements of IoT ecosystems. The paper also explores the interplay between emerging technologies like AI, quantum cryptography, and federated learning in enhancing M2M security. Additionally, the discussion highlights the growing role of edge computing and hybrid frameworks in improving both scalability and security. By addressing these elements, the research underscores the critical need for adaptive, cost-effective, and future-proof solutions in a rapidly evolving digital environment.