一项关于基于代优化RBF-NNN的网络安全情况预测的新研究
Yuqin Wu1, Congqi Shen2, Shungen Xiao3
1College of Information Engineering, Ningde Normal University, Ningde, China.
Scientific reports
|May 19, 2025
概括
本研究介绍了一种改进的辐射基函数神经网络 (RBF-NN) 用于网络安全情况 (NSS) 预测. 这种新的方法提高了预测准确度,并减少了复杂网络数据的训练时间.
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:
- 预测网络安全情况 (NSS) 对于减轻网络攻击至关重要.
- 现有的预测方法与非静止,非线性数据作斗争,导致缓慢的融合和局部最佳.
- 有限的概括能力阻碍了当前NSS预测模型的有效性.
研究的目的:
- 提出一种新的代优化辐射基函数神经网络 (RBF-NN),用于增强NSS预测.
- 解决现有方法的局限性,包括缓慢的融合和对局部最佳的敏感性.
- 提高复杂网络环境NSS预测的准确性和效率.
主要方法:
- 利用资源分配网络 (RAN) 来动态确定隐藏层神经元的最佳数量.
- 实现了交叉模型方法与遗传算法,以实现最佳的RBF-NN重量计算.
- 整合了一个混乱搜索策略,以防止模型在优化过程中汇聚到局部极端点.
主要成果:
- 在预测准确度方面取得了显著的改进,高达86.6%.
- 与现有技术相比,培训时间缩短了多达29.2%.
- 在现实世界NSS预测任务中表现出高效和有效的性能.
结论:
- 提出的代优化RBF-NN方法为NSS预测提供了卓越的性能.
- 整合RAN,遗传算法和混乱搜索有效地克服了以前方法的局限性.
- 该方法为主动网络安全提供了计算效率高和高度准确的解决方案.
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