Exploring AI and quantum computing synergies in holographic counterpart frameworks for IoT security and privacy

Exploring AI and quantum computing synergies in holographic counterpart frameworks for IoT security and privacy

연구 분야: Cryptography

학회: The Journal of Supercomputing

The Internet of Things (IoT) ecosystem, expected to surpass 75 billion connected consumer electronics devices by 2025, is revolutionizing industries while simultaneously introducing major security and privacy challenges. This study presents a framework that integrates artificial intelligence (AI), quantum computing (QC), and holographic counterpart modeling to improve IoT security. The traditional security mechanisms are inadequate to the peculiarities of IoT networks, including heterogeneous consumer electronics devices, limited computing capabilities, and more advanced attack vectors. This current study presents a new interdisciplinary approach that will address these issues by combining AI, QC, and holographic counterpart consumer technology applications on an all-purpose framework. The AI layer uses more sophisticated machine learning models, such as long short-term memory networks, and has an anomaly detection accuracy of 90.55%, which is much higher than the traditional models. Additionally, the framework also showed how the false positive rate was reduced to a value of 5%, enhancing reliability. QC enables cryptographic robustness by utilizing quantum key distribution to ensure 100% security in the encryption of data in the prevention of quantum-era malicious hacks. Grover’s algorithm improves encrypted data analysis speed by approximately threefold compared to classical approaches. The holographic counter dimensions come with a rare prediction building block; digital replicas of IoT networks are built in a dynamic manner, thereby cutting down on the possible vulnerabilities by 40% and generic threat resolution by 35%. The framework has been proven scalable and flexible in a variety of IoT setups, as experimental results were demonstrated to support more than 1 million data points and reach 98% correct classification of multiple types of attacks, such as distributed denial-of-service and malware intrusions. The AI-QC-HCF framework enhances IoT security by integrating AI-based adaptive learning, quantum computational techniques, and predictive holographic modeling, thereby supporting a shift from reactive to proactive threat mitigation. This study provides a robust, scalable, and proactive security architecture, addressing critical vulnerabilities in IoT ecosystems while paving the way for integrating advanced computational paradigms to mitigate evolving cyber threats.