ID 原文 译文
45226 针对 SDN 安全中的外部 DDoS 攻击问题进行研究，提出了一种基于深度学习混合模型的 DDoS 攻击检测方法——DCNN-DSAE。 Aiming at DDoS attack problem of security in SDN, a DDoS attack detection method called DCNN-DSAE based on deep learning hybrid model in SDN was proposed.
45227 该方法在构建深度学习模型时，输入特征除了从数据平面提取的 21 个不同类型的字段外，同时设计了能够区分流类型的 5 个额外流表特征。 In this method, when a deep learning model was constructed, the input feature included 21 different types of fields extracted from the data plane and 5 extra self-designed features of distinguishing flow types.
45228 实验结果表明，该方法具有较高的精确度，优于传统的支持向量机和深度神经网络等机器学习方法， The experimental results show that the method has high accuracy, it's better than the traditional support vector machine (SVM) and deep neural network (DNN) and other machine learning methods.
45229 同时，该方法还可以缩短分类检测的处理时间。 At the same time, the proposed method can also shorten the processing time of classification detection.
45230 将该检测模型部署于控制器中，利用检测结果产生新的安全策略，下发到 OpenFlow 交换机中，以实现对特定 DDoS 攻击的防御。 The de-tection model is deployed in SDN controller, and the new security policy is sent to the OpenFlow switch to achieve the defense against specific DDoS attack.
45231 为了解决船舶自组网应用条件下的消息认证问题，利用门限代理签名体制和双线性对性质，设计了一种不依赖于可信认证中心和防篡改设备的签名方案。 In order to solve the problem of message authentication under the conditions of the ship ad-hoc network(SANET), a signature scheme that does not depend on trusted certificate authorities and tamper-proof devices (TPD) was proposed by using the threshold proxy signature scheme and the properties of bilinear pairings.
45232 通过双重代理密钥的设计和门限签名机制的应用，使船舶节点通过多项式时间的计算完成消息签名， The proposed scheme used the dual-proxy key and the threshold signature mechanism to enable the ship nodes calculate the message signature in polynomial time.
45233 并运用随机预言模型证明了方案的安全性。 Moreover, the security of the scheme was also proved under the random oracle model.
45234 分析表明，该方案在保证正确性的前提下能满足强代理签名的性质，并具有较低的计算开销和通信开销。 The performance analysis results show that the proposed scheme can meet the requirement of strong proxy signature under the premise of guaranteeing correctness, and has lower computational cost and communication cost.
45235 兴趣点（POI, point of interest）推荐是位置社交网络（LBSN, location-based social network）重要的个性化服务，广泛用于热门景点推荐和旅游线路规划等。 POI (point of interest) recommendation is an important personalized service in the LBSN (location-based social network) which has wide applications such as popular sights recommendation and travel routes planning.