2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)最新文献_第10页

Security attack situation awareness based on massive log data mining 基于海量日志数据挖掘的安全攻击态势感知

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00116

Yin Xu, Pengfei Yu, Wen Shen, Ziqian Li

{"title":"Security attack situation awareness based on massive log data mining","authors":"Yin Xu, Pengfei Yu, Wen Shen, Ziqian Li","doi":"10.1109/AEMCSE55572.2022.00116","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00116","url":null,"abstract":"Security situation awareness usually uses massive log information to discover abnormal attacks based on basic user attributes, user behavioral actions and user interactions through machine learning and other methods. Considering that the interaction between users in security situation awareness is exactly the graph data structure to which graph neural networks are applicable, this paper proposes a graph neural network-based security situation awareness method for massive logs, by mining log data, extracting user features for aggregation, and finally predicting user behavior to achieve security situation awareness. Compared with traditional supervised or unsupervised learning algorithms, the graph structure built in this paper not only retains the information carried by the users themselves, but also retains the relationship features between users and users, and between users and servers. By mapping the relationships between users to homogeneous graphs and between users and servers to heterogeneous graphs, and introducing an attention mechanism to dynamically adjust the weights of neighboring nodes, the accuracy of graph neural network learning can be effectively improved.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124629620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Shortest Route of Material Transportation and Optimal Capital Savings Design Based on Floyd Algorithm 基于Floyd算法的物资运输最短路线及最优资金节约设计

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00103

Xingze Zhang, Zekun Chen, Liyan Xu, Xuetao Yan

引用次数: 0

Design of working stable for moxibustion robot based on improved TRIZ 基于改进TRIZ的艾灸机器人工作稳定性设计

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00034

Zhengyao Yi, Mengyuan Yang, Siyao Mi, Haoming Li

{"title":"Design of working stable for moxibustion robot based on improved TRIZ","authors":"Zhengyao Yi, Mengyuan Yang, Siyao Mi, Haoming Li","doi":"10.1109/AEMCSE55572.2022.00034","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00034","url":null,"abstract":"Due to the long sailing time and long time at sea, the crew members are susceptible to diseases such as rheumatism and high blood pressure. There are related moxibustion robots that can provide health care and treatment to patients, but the existing moxibustion health care robots have a fixed base, are cumbersome and can only be placed vertically. However, the ship running on the sea is generally affected by wind and waves with poor stability and serious roll and trim, which makes the relevant robot can not work normally and is difficult to be widely applied in the ship. Innovative design of a marine moxibustion robot stable base, which is function-oriented and attributes as the core by using “Unified Theory of the Solution of Inventive Problems(U-TRIZ)”. Combined with the analysis of the \"Substance-Analysis-Function-Cause(SAFC)\" model in the theory, the technical contradictions and physical contradictions in the design of the moxibustion robot base were extracted, and the harmful or insufficient functions were further extracted; The SAFC analysis model is converted to eliminate the harmful or insufficient functions, and a new design scheme of the bottom platform of the marine moxibustion robot is proposed to provide a reference scheme for solving the above problems.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"12 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120880803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiple Object Tracking in aerial vehicle overhead video 飞行器头顶视频中的多目标跟踪

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00032

Shaozhe Guo, Youshan Zhang, Yong Li, Yao Wang

引用次数: 0

Research on Pest Propagation Based on Auto-Regressive and Moving Average Model Algorithm 基于自回归和移动平均模型算法的害虫繁殖研究

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00081

Linlin Li, Binbin Dan, Ying Li, Wei Wang

{"title":"Research on Pest Propagation Based on Auto-Regressive and Moving Average Model Algorithm","authors":"Linlin Li, Binbin Dan, Ying Li, Wei Wang","doi":"10.1109/aemcse55572.2022.00081","DOIUrl":"https://doi.org/10.1109/aemcse55572.2022.00081","url":null,"abstract":"The presence of Vespa mandarinia can have a potentially serious impact on local bee populations and should be removed as soon as possible. In order to eradicate the Vespa mandarinia, we present several guidelines and strategies to help the State of Washington to allocate and utilize the limited resources efficiently.We describe our process in terms of CUU, a novel framework for Model Creation, Use and Update. On the basis of the ecological content of pests and the positive ID, negative ID and unprocessed data from the data table, we concluded that the spread of the pest changed over time. Afterwords, we infer that the range of hornet is small from the problem. So we utilize Auto-Regressive and Moving Average Model(ARMA) model as the time series prediction method and residual analysis to achieve the prediction accuracy in the paper. Later on, we classified the data utilizing K-nearest neighbor algorithm(KNN), and obtain that the unprocessed data were all Negative ID. Using the Positive ID data again, we select one of the points, calculate the average distance from the remaining 13 points to that point and calculate the probability of the presence of pests around that point, finally achieve the probability of mistaken classification: The smaller the average distance and the greater the probability of the presence of pests, the smaller the probability of the misclassification of data within 30 km of this area. Furthermore, to investigate the government’s desire to optimize resource allocation, entropy weight method and Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) method are proposed to score the locations where pests have been confirmed to appear. Finally, these locations are ranked by the score, where the harmful organisms are more likely to appear around the area when the score of this area is higher.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128115800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Modified D-LinkNet for Water Extraction from High-Resolution Remote Sensing 基于改进D-LinkNet的高分辨率遥感水体提取

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00038

Xueli Chang, Bo Deng, Zhixi Bao, Xinyi Guo, Fuxiang Yuan

引用次数: 0

Remote sensing image segmentation model based on attention mechanism 基于注意机制的遥感图像分割模型

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00086

Hanting Wang

引用次数: 0

Prediction of DNA methylation site status based on fusion deep learning algorithm 基于融合深度学习算法的DNA甲基化位点状态预测

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00044

Changde Wu, Hai Yang, Jinqiang Li, Feng Geng, Jianguo Bai, Chunling Liu, Wenjun Kao

{"title":"Prediction of DNA methylation site status based on fusion deep learning algorithm","authors":"Changde Wu, Hai Yang, Jinqiang Li, Feng Geng, Jianguo Bai, Chunling Liu, Wenjun Kao","doi":"10.1109/AEMCSE55572.2022.00044","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00044","url":null,"abstract":"DNA methylation is a crucial element of epigenetics and plays an important role in the evolution of life. As a result, detecting the status of DNA methylation becomes critically valuable. But since traditional biological experimental methods were unable to meet the actual needs, researchers began to employ machine learning and deep learning to aid biological experiments in determining methylation status. However, there are issues with feature acquisition, such as inconvenient extraction and high dimension. To address this issue, this paper proposes a feature extraction method based on convolution neural network (CNN) and recurrent neural network (RNN). Initially, the DNA methylation data used in this paper were obtained from the gene expression omnibus (GEO) database, and the data were preprocessed before use. Furthermore, we built a CNN and an RNN to extract features from DNA methylation data and then used feature splicing to find the best features. Eventually, we train the prediction model with a deep residual network and assess the model’s prediction performance with a confusion matrix. Compared with existing methods, we proposed method has better prediction performance.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130445926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Echo Chamber Super-Network in Online social media 在线社交媒体中的回声室超级网络

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00154

Ying Sun, Fengming Liu

引用次数: 0

Deep learning based on byte sample entropy for VPN encrypted traffic identification 基于字节样本熵的VPN加密流量识别深度学习

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00066

Yajuan Wang, Gengshen Yu, Wen Shen, Lintan Sun

{"title":"Deep learning based on byte sample entropy for VPN encrypted traffic identification","authors":"Yajuan Wang, Gengshen Yu, Wen Shen, Lintan Sun","doi":"10.1109/AEMCSE55572.2022.00066","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00066","url":null,"abstract":"Network traffic identification is important for traffic engineering, resource allocation, network management, attack detection and improving network QoS. However, with the rapid development of computer network technology, various VPN technologies and applications have emerged, which use encryption and decryption technology, tunneling technology and authentication technology to obfuscate and hide traffic characteristics, making VPN traffic difficult to identify. The recent rise of V2Ray makes up for and completes the shortcomings of previous VPN technologies with a more complete protocol, more robust performance and richer functionality, using V2Ray’s customised VMess protocol, and the VMess protocol supports TLS-based implementations, making it a full-featured and powerful application. These undoubtedly pose a huge challenge for network traffic identification and auditing, as well as a huge risk for network security. Therefore, the identification of VPN traffic is of great importance. In this paper, we propose a VPN traffic identification method based on byte sample entropy and session interaction time difference. We use the byte sample entropy and session interaction time difference of some message sequences in network traffic as feature data, and use Random Forest RF (RF) algorithm to identify V2Ray VMess traffic, TLS-based VMess traffic and ISCX VPN-NonVPN public dataset, achieving 95.97%, 90.32% and 91.78% recognition accuracy, respectively. The experimental results show that the method can be used for the detection and identification of V2Ray traffic, and also supports the detection and identification of the rest of VPN traffic.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130914000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0