Analytical metric weight generation for multi-domain trust in autonomous underwater MANETs
Analytical metric weight generation for multi-domain trust in autonomous underwater MANETs
复制标题
DOI：
10.1109/ucomms.2016.7583465
复制
发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
A. Bolster;A. Marshall
中科院分区：
文献类型：
--
作者：
A. Bolster;A. Marshall
信任管理框架(TMF)被用于使用从网络内节点的通信活动中获得的度量来提高分散的和分布式的自治MANET的效率、安全性和可靠性。然而，它们在稀疏/恶劣环境中表现不佳，例如在水声网络(UAN)中发现的那些[1]。随着节点能力的增加，节点的物理运动代表了关于网络操作和行为的额外知识领域。在本文中，我们提出了一种机器学习支持的方法来优化度量权重向量的生成，使用来自物理域和通信域的度量来检测和识别一系列不当行为，证明了通过利用来自多个域的信息，信任评估可以比单域(通信)评估更敏感和准确。
Trust Management Frameworks (TMFs) are being used to improve the efficiency, security, and reliability of decentralized and distributed autonomous MANETs using metrics garnered from the communications activities of nodes within the networks. However, these do not perform well in sparse / harsh environments such as those found in Underwater Acoustic Networks (UANs) [1]. As node capabilities increase, the physical motion of nodes represent an additional domain of knowledge about the operations and behaviours of the network. In this paper we present a Machine Learning supported methodology for optimising metric weight vector generation, using metrics from both physical and communications domains to detect and identify a range of misbehaviours, demonstrating that by utilising information from multiple domains, trust assessment can be more sensitive and accurate than in single-domain (communications) assessment.