通过动态优化和字符级深度学习在云环境中增强网络鱼检测
Vishnukumar Ravula1, Mangayarkarasi Ramaiah1
1School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology University, Vellore, Tamil Nadu, India.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了一种新的动态算术优化算法,使用深度学习驱动的网络鱼URL分类 (DAOA-DLPC) 模型来检测云支持的车辆互联网 (IoV) 环境中的网络鱼URL. 该DAOA-DLPC模型在识别安全和网络鱼URL方面实现了高精度,提高了安全性.
科学领域:
- 网络安全 网络安全
- 云计算 云计算 云计算 云计算
- 汽车的互联网 (IoV)
背景情况:
- 云计算和IoV环境面临着越来越多的网络鱼URL威胁.
- 传统的机器学习方法与动态的网络鱼格局作斗争.
- 由于网络鱼攻击,IoV中的服务可靠性受到威胁.
研究的目的:
- 提出一种新的动态算术优化算法,使用深度学习驱动的网络鱼URL分类 (DAOA-DLPC) 模型.
- 为了提高网络鱼URL检测在云支持的IoV基础设施.
- 为了提高识别恶意URL的准确性和效率.
主要方法:
- 利用字符级嵌入式进行有效的URL模式捕获.
- 集成嵌入式与多头注意力和双向门式循环单元 (MHA-BiGRU) 深度学习模型.
- 使用动态算术优化算法 (DAOA) 进行超参数调整.
主要成果:
- DAOA-DLPC模型实现了98.85%的准确性,98.49%的回忆率和98.38%的F1分数.
- 与动态环境中的传统模型相比,在动态环境中表现出优越的性能.
- 通过注意力机制和动态优化展示了计算效率.
结论:
- DAOA-DLPC模型为IoV中的网络鱼URL检测提供了可行和有效的解决方案.
- 该模型可以实时学习新的网络鱼攻击形式,并减少假阳性.
- 拟议的方法显著改善了安全和不安全的URL之间的区别.
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