IOT Security in Complex Systems: Big Data, Quantum Computing and HCI Design for AI Ethics

Abstract

Advanced security frameworks are required to protect networked devices due to the extraordinary data production caused by the IOT's rapid expansion in complex systems. In light of AI ethics, this research investigates the relationship between IOT security, Big Data analytics, Quantum Computing, and Human-Computer Interaction (HCI) design. IOT networks need real-time anomaly detection driven by Big Data algorithms and quantum-resistant cryptography solutions as they grow more susceptible to sophisticated cyber threats. Furthermore, by addressing concerns of transparency, bias reduction, and user trust, HCI design is essential to guaranteeing ethical AI interactions. This study suggests a multi-layered strategy for enhancing IOT security in complex environments by utilizing ethical AI-driven HCI frameworks, federated learning models, and quantum-enhanced security mechanisms. The results highlight how important it is to combine user-centric security designs, AI-driven threat intelligence, and quantum-safe encryption in order to build robust and morally sound IOT ecosystems. In a period of rapid technological advancement, this study offers a road map for legislators, researchers, and industry stakeholders to improve the security and moral governance of AI-driven IOT systems.

Keywords

Quantum-Resistant Cryptography-Internet of Things (IoT) Security -Complex Systems-Big Data Analytics-Quantum Computing