ID	原文	译文
3023	为解决内存映像中碎片证据文件提取问题，针对 doc、pdf 等常见文件类型，提出了一种基于内存映像的碎片文件雕刻模型。	To address the extraction of document evidence for doc, pdf, and other common file types in the memory im-age, the model of fragment file carving based on memory image was proposed.
3024	基于该模型，设计了基于文件对象结构链逆向的碎片文件雕刻算法，能够获取遗留在内存中的文件数据。	Then, on the basis of the model, the frag-ment file carving algorithm based on the reverse of file object structure chain was designed and implemented, the algo-rithm was able to get file data left behind in the memory image file.
3025	实验结果表明，该算法能够成功从内存映像中雕刻出文件相关的元数据信息，例如文件名、文件来源及操作行为等，雕刻精确度达到 100%；而且在典型应用情况下，文件内容数据雕刻精度达到 87.5％，远高于基于磁盘文件雕刻算法的精确度。	The experimental results show that the proposed al-gorithm can successfully carve out of memory file's metadata, and the accuracy is 100%, and in a typical application case,the accuracy of the algorithm for memory file can achieve 87.5%, far higher than disk-based file caving algorithm.
3026	针对车联社会网络（VSN）的通信安全问题，提出了一种高效的无证书签密方案，	To solve the communication security problems of vehicular social network (VSN), an efficient certificatelesssigncryption scheme was proposed.
3027	在随机预言模型下基于计算性 Diffie-Hellman 和椭圆曲线离散对数困难性问题证明了所提方案的安全性，为 VSN 成员间的通信提供了机密性和不可伪造性保护。	The proposed scheme was proven secure in the random oracle model based on the computational Diffie-Hellman problem and elliptic curve discrete logarithm problem, which provided confidentiality and unforgeability protection for VSN members.
3028	采用假名机制解决 VSN 中的隐私保护问题时，在不需要安装额外防篡改装置的条件下，提出了一种车辆假名及其密钥的自生成机制。	In addition, when the pseudonym mechanism was used to solve the privacyprotection problem in VSN, without installing tamper-proof device, a self-generation mechanism for vehicle pseudonyms and their keys was proposed.
3029	性能分析表明，所提方案可有效减少通信量，并可显著减少密钥生成中心的计算负担。	The performance analysis shows that the proposed scheme can decrease communicationcost, and significantly reduce the computation overhead of the key generation center.
3030	针对无人机自组网的拓扑时变、节点移动、间歇性连接等特点，提出用时序化图嵌入模型对预处理后的无人机自组网进行表征，基于线性概率计算采样间隔以提高采样效率，	Aiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying,node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessedUAANET. To improve the sampling efficiency, the sampling interval was calculated based on linear probability.
3031	将网络结构特征映射为节点间关系，并采用对抗训练提取节点上下文语义特征。	The network structure features were mapped to the relationship between nodes, and the contextual semantic features of nodeswere extracted by adversarial training.
3032	利用长短期记忆网络提取无人机自组网的时序特征，预测下一时刻的网络连接情况。	With the help of long and short-term memory network, the temporal characteristicsof the UAANET were extracted to predict the connection at the next moment. AUC, MAP, and Error Rate were employedas evaluation indexes.