一个安全的最糟糕的精英帆船鱼优化器基于路由和深度学习,用于黑洞攻击检测和检测
1Department of Computer Science & Engineering, I.K. Gujral Punjab Technical University, Kapurthala, India.
概括
本研究介绍了一种深度学习模型,用于检测和减轻无线传感器网络 (WSN) 中的黑洞攻击. 拟议的方法使用最糟糕的精英帆船鱼优化算法,以实现高效的路由和攻击检测.
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:
- 无线传感器网络 (WSN) 容易受到主动和被动攻击.
- 黑洞攻击对WSN的功能和数据完整性构成重大威胁.
- 现有的检测方法在识别和缓解这些攻击时可能缺乏效率.
研究的目的:
- 设计和实施基于深度学习 (DL) 的模型,用于检测和减轻WSN中的黑洞攻击.
- 增强网络安全并确保WSN中可靠的数据传输.
- 为了优化路由和攻击检测流程.
主要方法:
- 建立了一个WSN模拟环境.
- 最糟糕的精英帆船鱼优化 (WESFO) 算法用于最优的路由到基站 (BS).
- 使用WESFO训练的自动编码器 (AE) 模型被用于BS的黑洞攻击检测.
主要成果:
- 拟议的模型实现了25.64秒的低延迟.
- 记录了高达94.83%的包裹交付率 (PDR).
- 该模型证明了有效的攻击检测,假负率 (FNR) 为0.084和假阳性率 (FPR) 为0.135.
结论:
- 开发的基于DL的模型有效地检测和减轻WSN中的黑洞攻击.
- 将WESFO用于路由和AE用于检测的集成提高了网络性能和安全性.
- 该模型在PDR,延迟和准确性等关键性能指标方面显示出有希望的结果.
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