.png){kind=icon}
返回
Secure malicious node detection in flying ad-hoc networks using enhanced AODV algorithm
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-024-57480-6.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In wireless networking, the security of flying ad hoc networks (FANETs) is a major issue, and the use of drones is growing every day. A distributed network is created by a drone network in which nodes can enter and exit the network at any time. Because malicious nodes generate bogus identifiers, FANET is unstable. In this research study, we proposed a threat detection method for detecting malicious nodes in the network. The proposed method is found to be most effective compared to other methods. Malicious nodes fill the network with false information, thereby reducing network performance. The secure ad hoc on-demand distance vector (AODV) that has been suggested algorithm is used for detecting and isolating a malicious node in FANET. In addition, because temporary flying nodes are vulnerable to attacks, trust models based on direct or indirect reliability similar to trusted neighbors have been incorporated to overcome the vulnerability of malicious/selfish harassment. A node belonging to the malicious node class is disconnected from the network and is not used to forward or forward another message. The FANET security performance is measured by throughput, packet loss and routing overhead with the conventional algorithms of AODV (TAODV) and reliable AODV secure AODV power consumption decreased by 16.5%, efficiency increased by 7.4%, and packet delivery rate decreased by 9.1% when compared to the second ranking method. Reduced packet losses and routing expenses by 9.4%. In general, the results demonstrate that, in terms of energy consumption, throughput, delivered packet rate, the number of lost packets, and routing overhead, the proposed secure AODV algorithm performs better than the most recent, cutting-edge algorithms.
Keyword:
Flying ad-hoc network
Malicious node
Secure AODV
TAODV
AODV
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.3W
被引数:
83.5W
机构
引用论文
Infiltrating ductal carcinoma and synchronous malignant phyllodes tumour: Diagnostic and therapeutic approaches浸润性导管癌与同步性恶性叶状肿瘤：诊断和治疗策略
PREAI
分享收藏Robust and Reliable Predictive Routing Strategy for Flying Ad-Hoc Networks飞行ad-hoc网络鲁棒可靠的预测路由策略.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏Improving Source location privacy in social Internet of Things using a hybrid phantom routing technique使用混合幻影路由技术改善社交物联网中的源位置隐私COMPUTERS & SECURITY
COMPUTERS & SECURITYIF5.4
PREAI
分享收藏Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing使用基于拓扑的路由在飞行ad-hoc网络中进行动态路由DRONESOAAI
DRONESIF4.8
OAAI分享收藏Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networksSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏
查看更多内容