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<p>Optimizing security and performance in Non-Orthogonal Multiple Access (NOMA) networks through machine learning, addressing key challenges in enhancing efficiency and safeguarding user data in wireless systems. The integration of machine learning is essential to address security vulnerabilities and performance inefficiencies in NOMA networks, driven by increasing user demands and complex communication environments in modern wireless systems. To utilize machine learning for improving security and performance in NOMA networks, enhancing resource efficiency and safeguarding user data. Implement Intrusion Detection Systems (IDS) using machine learning techniques to detect and mitigate intrusions effectively. SCPM-GA optimizes security and performance in NOMA networks by adaptively managing power levels based on real-time conditions, ensuring robust encryption and authentication while enhancing resource utilization without compromising safety. Cross-layer optimisation (CLO) Optimize interactions between different layers of the network stack to improve overall system efficiency across network layers, leading to improved performance and resource utilization. The results show a high attack detection rate of 0.98 for Intrusion Detection Systems. Implementation using Python software is necessary for accurate analysis. Future research may focus on advanced machine learning for adaptive security, integrating edge computing, and creating enhanced privacy protocols to optimize NOMA network performance and user safety.</p>

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Optimizing security and performance in NOMA networks using machine learning

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Kolluru Suresh Babu,
  • R. Srilakshmi,
  • Suneetha Davuluri,
  • Venugopal Boppana,
  • Sanjay Gandhi Gundabatini,
  • Sri Hari Nallamala

摘要

Optimizing security and performance in Non-Orthogonal Multiple Access (NOMA) networks through machine learning, addressing key challenges in enhancing efficiency and safeguarding user data in wireless systems. The integration of machine learning is essential to address security vulnerabilities and performance inefficiencies in NOMA networks, driven by increasing user demands and complex communication environments in modern wireless systems. To utilize machine learning for improving security and performance in NOMA networks, enhancing resource efficiency and safeguarding user data. Implement Intrusion Detection Systems (IDS) using machine learning techniques to detect and mitigate intrusions effectively. SCPM-GA optimizes security and performance in NOMA networks by adaptively managing power levels based on real-time conditions, ensuring robust encryption and authentication while enhancing resource utilization without compromising safety. Cross-layer optimisation (CLO) Optimize interactions between different layers of the network stack to improve overall system efficiency across network layers, leading to improved performance and resource utilization. The results show a high attack detection rate of 0.98 for Intrusion Detection Systems. Implementation using Python software is necessary for accurate analysis. Future research may focus on advanced machine learning for adaptive security, integrating edge computing, and creating enhanced privacy protocols to optimize NOMA network performance and user safety.