Software-defined networking(SDN)enables the separation of control and data planes,allowing for centralized control and management of the network.Without adequate access control methods,the risk of unau-thorized access...Software-defined networking(SDN)enables the separation of control and data planes,allowing for centralized control and management of the network.Without adequate access control methods,the risk of unau-thorized access to the network and its resources increases significantly.This can result in various security breaches.In addition,if authorized devices are attacked or controlled by hackers,they may turn into malicious devices,which can cause severe damage to the network if their abnormal behaviour goes undetected and their access privileges are not promptly restricted.To solve those problems,an anomaly detection and access control mechanism based on SDN and neural networks is proposed for cloud-edge collaboration networks.The system employs the Attribute Based Access Control(ABAC)model and smart contract for fine-grained control of device access to the network.Furthermore,a cloud-edge collaborative Key Performance Indicator(KPI)anomaly detection method based on the Gated Recurrent Unit and Generative Adversarial Nets(GRU-GAN)is designed to discover the anomaly devices.An access restriction mechanism based on reputation value and anomaly detection is given to prevent anomalous devices.Experiments show that the proposed mechanism performs better anomaly detection on several datasets.The reputation-based access restriction effectively reduces the number of malicious device attacks.展开更多
城市道路短时车速预测是智能交通系统中的一个重要组成部分,也是城市道路交通信号灯控制、公交调度、出行线路搜索等具体应用任务的基础.目前这方面的研究热点多是使用图卷积神经网络捕获空间特征,再使用直接的线性拼接方法进行特征处理...城市道路短时车速预测是智能交通系统中的一个重要组成部分,也是城市道路交通信号灯控制、公交调度、出行线路搜索等具体应用任务的基础.目前这方面的研究热点多是使用图卷积神经网络捕获空间特征,再使用直接的线性拼接方法进行特征处理,在准确度上有不足.针对短时车速预测的高准确性要求,提出一种融合图注意力网络、门控循环单元和天气权重因子的深度学习预测模型(Graph Attention Network Based with Weather Weight,W2-GAT).其中,图注意力网络利用注意力机制捕获城市道路空间信息,门控循环单元用于提取车速时间特征,重点关注待预测点周边的路面车速情况;在特征处理方面,将天气因素作为可变权重超参数与具体的道路车速特征进行融合,提高预测的准确性.实验结果表明,和现有模型相比,W2-GAT模型预测结果的均方根误差平均降低7.5%,准确率平均提升4%,能够较好地反映实际路面情况下未来短时的车速特征,为具体应用提供数据支撑.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62162018 and Grant 61861013in part by the Innovation Research Team Project of Guangxi Natural Science Foundation 2019GXNSFGA245004.
文摘Software-defined networking(SDN)enables the separation of control and data planes,allowing for centralized control and management of the network.Without adequate access control methods,the risk of unau-thorized access to the network and its resources increases significantly.This can result in various security breaches.In addition,if authorized devices are attacked or controlled by hackers,they may turn into malicious devices,which can cause severe damage to the network if their abnormal behaviour goes undetected and their access privileges are not promptly restricted.To solve those problems,an anomaly detection and access control mechanism based on SDN and neural networks is proposed for cloud-edge collaboration networks.The system employs the Attribute Based Access Control(ABAC)model and smart contract for fine-grained control of device access to the network.Furthermore,a cloud-edge collaborative Key Performance Indicator(KPI)anomaly detection method based on the Gated Recurrent Unit and Generative Adversarial Nets(GRU-GAN)is designed to discover the anomaly devices.An access restriction mechanism based on reputation value and anomaly detection is given to prevent anomalous devices.Experiments show that the proposed mechanism performs better anomaly detection on several datasets.The reputation-based access restriction effectively reduces the number of malicious device attacks.
文摘城市道路短时车速预测是智能交通系统中的一个重要组成部分,也是城市道路交通信号灯控制、公交调度、出行线路搜索等具体应用任务的基础.目前这方面的研究热点多是使用图卷积神经网络捕获空间特征,再使用直接的线性拼接方法进行特征处理,在准确度上有不足.针对短时车速预测的高准确性要求,提出一种融合图注意力网络、门控循环单元和天气权重因子的深度学习预测模型(Graph Attention Network Based with Weather Weight,W2-GAT).其中,图注意力网络利用注意力机制捕获城市道路空间信息,门控循环单元用于提取车速时间特征,重点关注待预测点周边的路面车速情况;在特征处理方面,将天气因素作为可变权重超参数与具体的道路车速特征进行融合,提高预测的准确性.实验结果表明,和现有模型相比,W2-GAT模型预测结果的均方根误差平均降低7.5%,准确率平均提升4%,能够较好地反映实际路面情况下未来短时的车速特征,为具体应用提供数据支撑.
