Trust Quantification in a Collaborative Drone System with Intelligence-driven Edge Routing
Trust Quantification in a Collaborative Drone System with Intelligence-driven Edge Routing
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DOI：
10.1109/noms56928.2023.10154317
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发表时间：
2023-05
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影响因子：
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通讯作者：
Alicia Esquivel Morel;Ekincan Ufuktepe;Cameron Grant;Samuel Elfrink;Chengyi Qu;P. Calyam;K. Palaniappan
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作者：
Alicia Esquivel Morel;Ekincan Ufuktepe;Cameron Grant;Samuel Elfrink;Chengyi Qu;P. Calyam;K. Palaniappan
协同无人机系统（CDS）有可能使农业、军事行动、监视和灾难响应等各种应用领域受益。与此同时，CDS可能会带来挑战，因为它们有限的飞行时间受到电池容量的影响，并且无人机上的边缘计算能力受到限制。此外，一个未充分研究的主题涉及CDS中的无人机何时相互信任以完成任务，从而导致可以通过网络攻击利用的新漏洞。在本文中，我们提出了一种新的信任量化方法，在CDS与智能驱动的边缘路由，它可以帮助检测恶意节点的CDS，妥协的通信和破坏的功能，数据包转发。我们的信任量化方法是由CDS脆弱性分析，其特征的影响，由于存在两个恶意威胁代理，即，伪节点和伪节点。CDS中这些威胁代理的检测通过信任量化来辅助，所述信任量化以通过使用贝叶斯网络模型获得的信任分数的形式，所述贝叶斯网络模型允许对CDS节点的信任级别进行决策。我们验证了我们的信任量化方法在ns-3的模拟实验，并显示我们如何可以根据不同的阈值的信任分数不同的敏感性，这有助于在检测CDS威胁代理节点进行分类。
Collaborative Drone systems (CDS) have the potential to benefit a variety of application areas such as agriculture, military operations, surveillance, and disaster response. At the same time, CDS can pose challenges due to their limited flight time impacted by battery capacities, and constrained edge computation capabilities on-board the drones. Furthermore, an understudied subject relates to when drones in a CDS trust each other to accomplish a task, resulting in new vulnerabilities that can be exploited via cyber attacks. In this paper, we propose a novel trust quantification methodology in a CDS with intelligence-driven edge routing, which can help detect malicious nodes in a CDS that compromise communication and disrupt the functionality of packet forwarding. Our approach for trust quantification is guided by a CDS vulnerability analysis that characterizes impact due to the presence of two malicious threat agents viz., flooder node and faker node. Detection of these threat agents in a CDS is aided by trust quantification in the form of trust scores obtained by using a Bayesian Network model that allows for decision-making on CDS nodes’ trust levels. We validate our trust quantification methodology in ns-3 based simulation experiments and show how we can categorize nodes based on different thresholds of trust scores with varying sensitivities, which helps in the detection of CDS threat agents.