论文标题
在洛拉网络的背景下,低功率广泛区域网络的安全问题
Security Issues of Low Power Wide Area Networks in the Context of LoRa Networks
论文作者
论文摘要
低功率广泛的区域网络(LPWAN)已用于支持物联网(IoT),智能城市和广泛的工业应用的低成本和移动双向通信。 LPWAN技术的主要安全问题是攻击是阻止节点之间合法通信的攻击,从而导致方案,例如数据包丢失,延迟数据包到达以及偏斜的数据包到达报告网关。洛拉(远程)是一种有前途的无线无线电访问技术,它支持低数据速率和低功耗的远程通信。洛拉被认为是建造LPWAN的理想候选人之一。我们使用洛拉(Lora)作为参考技术来审查空中的物联网安全威胁以及到目前为止采用的不同对策的适用性。靠近网关的Lora节点使用的SF小于遥远的节点。但这也意味着长时间的空中传输时间,这使得传输的数据包容易受到不同种类的恶意攻击,尤其是在物理和链接层中。因此,由于它们具有不同级别的漏洞,因此不可能为所有Lora节点执行固定的规则。我们的调查表明,迫切需要在最终设备和网关之间进行安全和不间断的通信,尤其是当威胁模型事先未知时。我们探索传统的对策,发现其中大多数现在无效,例如跳跃和传播频谱方法。为了适应新的威胁,使用游戏理论方法和加强机器学习方法的新兴对策可以有效地识别威胁,并动态选择相应的动作以抵抗威胁,从而进行安全且可靠的沟通。
Low Power Wide Area Networks (LPWAN) have been used to support low cost and mobile bi-directional communications for the Internet of Things (IoT), smart city and a wide range of industrial applications. A primary security concern of LPWAN technology is the attacks that block legitimate communication between nodes resulting in scenarios like loss of packets, delayed packet arrival, and skewed packet reaching the reporting gateway. LoRa (Long Range) is a promising wireless radio access technology that supports long-range communication at low data rates and low power consumption. LoRa is considered as one of the ideal candidates for building LPWANs. We use LoRa as a reference technology to review the IoT security threats on the air and the applicability of different countermeasures that have been adopted so far. LoRa nodes that are close to the gateway use a small SF than the nodes which are far away. But it also implies long in-the-air transmission time, which makes the transmitted packets vulnerable to different kinds of malicious attacks, especially in the physical and the link layer. Therefore, it is not possible to enforce a fixed set of rules for all LoRa nodes since they have different levels of vulnerabilities. Our survey reveals that there is an urgent need for secure and uninterrupted communication between an end-device and the gateway, especially when the threat models are unknown in advance. We explore the traditional countermeasures and find that most of them are ineffective now, such as frequency hopping and spread spectrum methods. In order to adapt to new threats, the emerging countermeasures using game-theoretic approaches and reinforcement machine learning methods can effectively identify threats and dynamically choose the corresponding actions to resist threats, thereby making secured and reliable communications.