A delay-tolerant network architecture for edge computing with applications in narrow band internet of things

Ethical ref# 204520231/2023/3The research involved designing and testing a narrow-band IoT Delay-Tolerant Network (NB-IoTDTN) to enhance resilience against Distributed Denial-of-Service (DDoS) attacks. The data consisted of simulated network traffic and performance metrics collected from a testbed environment, which was built using Raspberry Pi nodes connected in a K3s edge cluster. The nodes were configured to run containerized environments using Cilium CNI for secure and observable networking.Type of Data: The collected data included network performance metrics such as latency, jitter, packet loss, throughput, and system logs detailing DDoS attack attempts and mitigations. This data was captured using monitoring tools like Grafana and Prometheus.Data Collection: Network traffic, including normal and DDoS-attack scenarios, was simulated using UERANSIM and Open5GS to replicate the interaction between NB-IoT devices and the core network. Data was collected continuously during these simulations to monitor the network's ability to maintain performance under attack conditions.Usage of Data: The data was used to evaluate the effectiveness of the NB-IoTDTN architecture in mitigating the impact of DDoS attacks. Key metrics such as system uptime, data packet delivery rates, and service continuity under attack conditions were analyzed.Outcome: The findings from this data indicated that the NB-IoTDTN architecture significantly improved the network's resilience by maintaining service continuity during DDoS scenarios. The lightweight security protocols designed for resource-constrained devices showed effectiveness with minimal computational overhead. The data demonstrated improved performance in maintaining network functionality even under high-traffic conditions caused by DDoS attacks.

伦理编号 #204520231/2023/3 本项研究涉及设计并测试一种针对分布式拒绝服务（DDoS）攻击的抗攻击性增强的窄带物联网延迟容忍网络（NB-IoTDTN）。数据集包含模拟网络流量以及从构建于Raspberry Pi节点、以K3s边缘集群连接的测试平台环境中收集的性能指标。节点被配置为运行使用Cilium CNI进行安全且可观察网络连接的容器化环境。数据类型：所收集的数据包括网络性能指标，如延迟、抖动、数据包丢失率、吞吐量，以及详细记录DDoS攻击尝试和缓解措施的系统日志。这些数据是通过Grafana和Prometheus等监控工具捕获的。数据收集：使用UERANSIM和Open5GS模拟网络流量，包括正常和DDoS攻击场景，以复制NB-IoT设备与核心网络之间的交互。在模拟过程中持续收集数据，以监控网络在攻击条件下的性能维持能力。数据使用：这些数据被用于评估NB-IoTDTN架构在缓解DDoS攻击影响方面的有效性。关键指标，如系统正常运行时间、数据包投递率和攻击条件下的服务连续性，均得到了分析。结果：从这些数据中获得的发现表明，NB-IoTDTN架构通过在DDoS场景下维持服务连续性，显著提高了网络的抗攻击性。为资源受限设备设计的轻量级安全协议表现出在最小计算开销下的有效性。数据展示了即使在DDoS攻击引起的高流量条件下，也能保持网络功能性能的改进。