基于传感器启用网络的优化时钟周期反复神经网络的网络安全情况预测
Xiuli Du1, Xiaohui Ding1, Fan Tao1
1Communication and Network Laboratory, Dalian University, Dalian 116622, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一种优化的时钟循环神经网络 (CW-RNN),用于网络安全,提高预测准确性和实时性能. 通过使用一种新的时钟周期机制,CW-RNN有效地捕捉短期和长期的网络动态.
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:
- 网络安全情况表现出复杂的时间动态和非线性.
- 准确的预测和实时监控对于有效的网络安全至关重要.
- 现有的模型可能很难有效地捕捉短期和长期的依赖关系.
研究的目的:
- 为网络安全预测提出一个优化的时钟循环神经网络 (CW-RNN).
- 提高模型捕捉时间特征和非线性动态的能力.
- 提高网络安全监控中的预测准确度和实时性能.
主要方法:
- 实现了一个时钟式循环神经网络 (CW-RNN) 架构.
- 在隐藏的单元中使用时钟循环机制来处理不同频率的信息.
- 使用灰狼优化 (GWO) 算法进行超参数调整.
- 评估模型在网络安全情况数据上的表现.
主要成果:
- 优化的CW-RNN在提取时间和非线性特征方面表现出卓越的性能.
- 与其他网络模型相比,该模型实现了更好的预测准确性.
- 该方法表现出较低的时间复杂性和卓越的实时性能.
- 在网络数据中有效地捕获了短期和长期的时间依赖.
结论:
- 拟议的CW-RNN与GWO优化为网络安全预测提供了有效的解决方案.
- 时钟周期机制增强了模型学习复杂时间模式的能力.
- 该方法适用于实时监控大规模网络流量,特别是在传感器网络中.
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