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@article{183990,
        author = {Dr M. V. Siva Prasad},
        title = {Big Data and Quantum Neural Network-Based Reliable Safety of Wireless Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {4007-4011},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183990},
        abstract = {Wireless networks are at the core of modern communication infrastructure, yet they face increasing security threats due to their openness and dynamic nature. Traditional security mechanisms are increasingly inadequate for the ever-expanding and complex data environments. This paper proposes an innovative framework combining Big Data analytics with a Quantum Neural Network (QNN) to ensure the reliable safety of wireless networks. The proposed architecture leverages massive data collection, intelligent analytics, and quantum-enhanced learning for real-time threat detection, mitigation, and adaptive security responses.
The exponential growth in wireless communication and the increasing complexity of cyber threats call for robust safety frameworks. Traditional security models fall short in addressing real-time threats, particularly with the rise of big data and dynamic network environments. This paper proposes a hybrid approach integrating Big Data analytics with Quantum Neural Networks (QNNs) to ensure the reliable safety of wireless networks. The model leverages big data's volume, variety, and velocity characteristics to feed enriched data into QNNs for intelligent, real-time threat detection and adaptive countermeasure formulation. Simulation results show that the proposed framework achieves superior detection accuracy and reduced response time compared to classical methods.},
        keywords = {},
        month = {August},
        }