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使用增强的AODV算法在飞行临时网络中安全检测恶意节点
V Chandrasekar1, V Shanmugavalli2, T R Mahesh1
1Department of Computer Science & Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, 562112, India.
Scientific reports
|April 3, 2024
概括
本研究介绍了一种安全的AODV算法,用于检测和隔离飞行特设网络 (FANET) 中的恶意节点. 拟议的方法通过减少数据包丢失和路由开销来提高网络安全性和性能.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 无线通信无线通信
背景情况:
- 飞行特设网络 (FANET) 的使用越来越多,但由于动态节点行为和恶意攻击,它们面临着重大安全挑战.
- 恶意节点注入虚假信息破坏FANET的稳定性,降低网络性能和可靠性.
研究的目的:
- 提出和评估一种新的威胁检测方法,用于识别和隔离FANET中的恶意节点.
- 通过使用安全的Ad hoc按需距离向量 (AODV) 算法来提高FANET的安全性和效率.
主要方法:
- 实现了一个安全的AODV算法,包含基于直接/间接可靠性的信任模型,用于节点验证.
- 开发了一个威胁检测机制,以识别和断开恶意节点的网络连接.
- 评估性能指标,包括吞吐量,数据包丢失和路由开销与传统的AODV算法相比.
主要成果:
- 提出的安全的AODV算法在能源消耗,吞吐量,数据包交付速度方面表现出卓越的性能,并减少了数据包丢失和路由开销.
- 与排名第二的方法相比,实现了电力消耗降低16.5%,效率提高7.4%,数据包交付率降低9.1%.
- 报告说,数据包丢失和路由费用减少了9.4%.
结论:
- 开发的安全AODV算法可以有效地检测和隔离FANET中的恶意节点.
- 拟议的方法显著改善了FANET的安全性和性能指标,与现有的算法相比.
- 这项研究为增强无人机无线网络的稳定性和可靠性提供了强大的解决方案.
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