电信网络异常检测异常检测的混合算术优化算法
1Department of Computer Science, Mogadishu University, Mogadishu, Somalia. cigaleh@mu.edu.so.
Scientific reports
|November 18, 2025
概括
本研究介绍了OBAOANM,一种结合基于对立的学习,Nelder-Mead和算术优化算法的新算法,以显著提高电信网络入侵检测准确性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 电信网络面临着复杂的异常和入侵.
- 现有的入侵检测系统 (NID) 需要提高准确性和效率.
- 网络流量分析对于维护网络完整性至关重要.
研究的目的:
- 引入一种混合算法,OBAOANM,用于卓越的电信网络异常检测.
- 提高算术优化算法 (AOA) 的利用和探索能力.
- 为了提高网络绕道异常和整体NID的识别精度.
主要方法:
- 基于对立的学习 (OB) 和Nelder-Mead (NM) 算法的混合化与算术优化算法 (AOA).
- 开发OBAOANM算法,以扩展AOA的阶段,以便更好地识别异常.
- 使用OBAOANM进行网络流量分析和入侵检测.
主要成果:
- OBAOANM算法证明了对网络绕道异常的增强识别.
- 在比较的NID实验中达到93.10%的平均分类准确率.
- 提出的方法在最大限度地提高NID.的分类准确度方面被证明是有效的.
结论:
- OBAOANM是一个强大的工具,用于解决电信环境中的网络流量问题.
- 混合方法显著提高了电信网络入侵检测的精度.
- 该算法为复杂的网络安全挑战提供了更合适的解决方案.
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