提高移动特设网络的安全性和效率,使用混合深度学习模型进行洪水攻击检测和检测
Pramodh Krishna D1, E Sandhya2, Khaja Shareef Sk3
1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India.
Scientific reports
|January 4, 2025
概括
本研究介绍了一种混合深度学习模型,用于移动广告 hoc 网络 (MANETs) 打击洪水攻击. 这种新的方法提高了网络安全性,改善了数据传输,节约了能源,优于传统方法.
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:
- 移动特设网络 (MANET) 提供了分散的通信,但易受洪水攻击的影响.
- 洪水袭击通过降低性能和耗尽能源来破坏MANET.
研究的目的:
- 开发一种混合深度学习模型,用于在MANET中检测和减轻洪水攻击.
- 提高MANET的安全性,可靠性和能源效率.
主要方法:
- 整合卷积神经网络 (CNN),长期短期记忆 (LSTM) 和门式循环单元 (GRU) 架构.
- 使用一个新的DECEHGS算法 (差异进化和进化人口动力学) 的优化.
主要成果:
- 在检测恶意节点方面取得了95%的准确性.
- 增加了12%的包裹交付比率.
- 与传统方法相比,降低了20%的路由开销.
结论:
- 拟议的混合深度学习模型为MANET安全提供了有效和节能的解决方案.
- 在网络性能和对抗攻击的稳定性方面取得了显著的改善.
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