QuantumShield framework: Pioneering resilient security in IoT networks through quantum-resistant cryptography and federated learning techniques
Main Article Content
Abstract
The ever-expanding realm of IoT networks presents a double-edged sword. While it offers a captivating vision
of a seamlessly interconnected world, it also introduces critical security challenges. The resource-constrained nature of these
devices, often lacking processing power and robust security features, makes them highly susceptible to traditional
cyberattacks. Additionally, the vast scale of IoT deployments, encompassing billions of devices, necessitates robust and
scalable security solutions that can function efficiently without overwhelming the limited resources of individual devices.
This paper proposes the QuantumShield framework, a pioneering approach that leverages the combined strengths of two
emerging technologies: quantum-resistant cryptography (QRC) and federated learning (FL). QRC offers a critical safeguard
against a future dominated by quantum computers. Traditional cryptographic algorithms are demonstrably vulnerable to
attacks mounted by these powerful machines, and QRC steps in to provide demonstrably secure alternatives. Federated
learning, on the other hand, empowers collaborative learning across distributed devices within the network. This enables the
development of intrusion detection systems that can identify anomalies and security threats without compromising the
sensitive data stored on individual devices. By integrating these powerful paradigms, the QuantumShield framework aims to
revolutionize the security landscape of IoT networks. We will delve deeper into the design and implementation of the
QuantumShield framework, evaluate its performance using relevant metrics on a suitable dataset, and explore its potential
impact on securing future large-scale IoT deployments.
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