A novel Q-learning-based secure routing scheme with a robust defensive system against wormhole attacks in flying ad hoc networks

IF 5.8 2区 计算机科学 Q1 TELECOMMUNICATIONS
Mehdi Hosseinzadeh , Saqib Ali , Husham Jawad Ahmad , Faisal Alanazi , Mohammad Sadegh Yousefpoor , Efat Yousefpoor , Omed Hassan Ahmed , Amir Masoud Rahmani , Sang-Woong Lee
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引用次数: 0

Abstract

Nowadays, unmanned aerial vehicles (UAVs) organized in a flying ad hoc network (FANET) can successfully carry out complex missions. Due to the limitations of these networks, including the lack of infrastructure, wireless communication channels, dynamic topology, and unreliable communication between UAVs, cyberattacks, especially wormholes, weaken the performance of routing schemes. Therefore, maintaining communication security and guaranteeing the quality of service (QoS) are very challenging. In this paper, a novel Q-learning-based secure routing scheme (QSR) is presented for FANETs. QSR seeks to provide a robust defensive system against wormhole attacks, especially wormhole through encapsulation and wormhole through packet relay. QSR includes a secure neighbor discovery process and a Q-learning-based secure routing process. Firstly, each UAV gets information about its neighboring UAVs securely. To secure communication in this process, a local monitoring system is designed to counteract the wormhole attack through packet relay. This system checks data packets exchanged between neighboring UAVs and defines three rules according to the behavior of wormholes. In the second process, UAVs perform a distributed Q-learning-based routing process to counteract the wormhole attack through encapsulation. To reward the safest paths, a reward function is introduced based on five factors, the average one-hop delay, hop count, data loss ratio, packet transmission frequency (PTF), and packet reception frequency (PRF). Finally, the NS2 simulator is applied for implementing QSR and executing different scenarios. The evaluation results show that QSR works better than TOPCM, MNRiRIP, and MNDA in terms of accuracy, malicious node detection rate, data delivery ratio, and data loss ratio. However, it has more delay than TOPCM.

基于 Q-learning 的新型安全路由方案,以及针对飞行 ad hoc 网络中虫洞攻击的稳健防御系统
如今,在飞行临时网络(FANET)中组织起来的无人驾驶飞行器(UAV)可以成功执行复杂的任务。由于这些网络的局限性,包括缺乏基础设施、无线通信信道、动态拓扑以及无人飞行器之间的通信不可靠,网络攻击,特别是虫洞,削弱了路由方案的性能。因此,维护通信安全和保证服务质量(QoS)非常具有挑战性。本文针对 FANET 提出了一种基于 Q-learning 的新型安全路由方案(QSR)。QSR 试图提供一种稳健的防御系统来抵御虫洞攻击,尤其是通过封装的虫洞和通过数据包中继的虫洞。QSR 包括安全邻居发现过程和基于 Q 学习的安全路由过程。首先,每个无人机安全地获取其相邻无人机的信息。为了确保这一过程中的通信安全,设计了一个本地监控系统,通过数据包中继来抵御虫洞攻击。该系统会检查相邻无人机之间交换的数据包,并根据虫洞行为定义三条规则。在第二个过程中,无人机执行基于 Q-learning 的分布式路由过程,通过封装抵御虫洞攻击。为了奖励最安全的路径,引入了基于平均单跳延迟、跳数、数据丢失率、数据包发送频率(PTF)和数据包接收频率(PRF)这五个因素的奖励函数。最后,应用 NS2 模拟器实现 QSR 并执行不同的场景。评估结果表明,QSR 在准确率、恶意节点检测率、数据传送率和数据丢失率方面都优于 TOPCM、MNRiRIP 和 MNDA。但是,它比 TOPCM 有更多的延迟。
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来源期刊
Vehicular Communications
Vehicular Communications Engineering-Electrical and Electronic Engineering
CiteScore
12.70
自引率
10.40%
发文量
88
审稿时长
62 days
期刊介绍: Vehicular communications is a growing area of communications between vehicles and including roadside communication infrastructure. Advances in wireless communications are making possible sharing of information through real time communications between vehicles and infrastructure. This has led to applications to increase safety of vehicles and communication between passengers and the Internet. Standardization efforts on vehicular communication are also underway to make vehicular transportation safer, greener and easier. The aim of the journal is to publish high quality peer–reviewed papers in the area of vehicular communications. The scope encompasses all types of communications involving vehicles, including vehicle–to–vehicle and vehicle–to–infrastructure. The scope includes (but not limited to) the following topics related to vehicular communications: Vehicle to vehicle and vehicle to infrastructure communications Channel modelling, modulating and coding Congestion Control and scalability issues Protocol design, testing and verification Routing in vehicular networks Security issues and countermeasures Deployment and field testing Reducing energy consumption and enhancing safety of vehicles Wireless in–car networks Data collection and dissemination methods Mobility and handover issues Safety and driver assistance applications UAV Underwater communications Autonomous cooperative driving Social networks Internet of vehicles Standardization of protocols.
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