基于深度学习的无人机自组网分层入侵检测方法
Layered intrusion detection method for unmanned aerial vehicle Ad Hoc networks based on deep learning
杜玉航 1王伟1
作者信息
摘要
针对无人机自组网携带重要信息却容易受到各种攻击的问题,提出了一种分层检测响应的方案.首先采用端节点误用检测方法,监测无人机行为并报告规则验证的结果.接着,地面站利用深度神经网络分类算法进一步优化验证结果.为抵御如干扰、黑洞、灰洞等不同类型网络攻击提供了新的解决方案.最后,与BRUIDS方案和分布式检测方案进行模拟分析和实验对比.结果表明,相较于另外两种方案,方法在检测率方面的下降率均保持在10%以内,检测率高达93%以上.误报率提升了1.2%,在检测攻击延迟方面与分布式方案相差无几,通信开销减少了约53.5 KBps,且对未知攻击检测问题有显著改善.
Abstract
This paper proposes a layered detection response scheme to address the problem of unmanned aerial vehicle(UAV)self-organizing networks carrying important information but being susceptible to various attacks.Firstly,the end node misuse detection method is adopted to monitor the behavior of drones and report the results of rule validation.Subsequently,the ground station utilized deep neural network classification algorithms to further optimize the validation results.Provided new solutions to resist different types of network attacks such as interference,black holes,and gray holes.Finally,this article conducts simulation analysis and experimental comparison with the BRUIDS scheme and distributed detection scheme.The results show that compared to the other two schemes,the decrease rate of detection rate in our method remains within 10%,with a detection rate of over 93%.The false alarm rate has increased by 1.2%,which is almost the same as the distributed scheme in detecting attack latency.The communication overhead has been reduced by about 53.5 KBps,and there is a significant improvement in the detection of unknown attacks.
关键词
深度学习/分层检测/入侵检测/无人机自组网Key words
deep learning/layered detection/intrusion detection/unmanned aerial vehicle引用本文复制引用
出版年
2024
NETL
NSTL
万方数据