人工智能驱动的入侵检测和预防系统,以保护6G网络免受网络威胁
P Chinnasamy1, Sarojini Yarramsetti2, Ramesh Kumar Ayyasamy3
1Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Srivilliputtur, Tamil Nadu, India.
Scientific reports
|October 30, 2025
概括
本研究引入了一种新的机器学习方法,用于检测未来6G无线网络中的网络攻击. 拟议的系统显著提高了入侵检测准确性和6G安全的网络效率.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 无线通信无线通信
背景情况:
- 第六代 (6G) 无线网络正在出现,其容量和效率要求很高.
- 复杂的网络攻击需要6G网络的先进安全措施.
- 侵入检测是确保6G基础设施的关键挑战.
研究的目的:
- 提出一种基于机器学习的新型系统,用于监控6G网络中的网络攻击和入侵检测.
- 提高6G网络的安全性和效率,以应对复杂的网络威胁.
- 评估拟议的入侵检测系统的性能.
主要方法:
- 实施了一个区块链联合的高斯多代理Q编码器神经网络 (BFGMAQENN) 用于入侵检测.
- 利用鱼群二元狼优化 (WSBWO) 进行6G网络优化.
- 对各种网络攻击数据集进行实验分析.
主要成果:
- 实现了97%的网络攻击检测准确度.
- 显示了94%的数据完整性和93%的可扩展性.
- 报告了60%的通讯开销和98%的网络效率.
结论:
- 拟议的BFGMAQENN和WSBWO模型为6G网络入侵检测提供了一个强大的解决方案.
- 该系统有效地平衡了高检测率与网络性能指标.
- 这项研究为确保下一代无线通信系统的安全提供了基础.
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