2022 International Conference on Networking and Network Applications (NaNA)最新文献_第4页

Pedestrian Detection Based on Improved SSD Object Detection Algorithm 基于改进SSD目标检测算法的行人检测

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00101

Yunchuan Wu, Cheng Chen, Bo Wang

{"title":"Pedestrian Detection Based on Improved SSD Object Detection Algorithm","authors":"Yunchuan Wu, Cheng Chen, Bo Wang","doi":"10.1109/NaNA56854.2022.00101","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00101","url":null,"abstract":"Pedestrian detection is an important application of object detection. SSD is one of the target detection algorithms based on deep learning with better performance. The weak detection ability of SSD for small objects, and there will still be false detections and missed detections in the detection situation of the complex environment. In order to improve the detection accuracy of SSD for pedestrians, we propose an improved SSD object detection algorithm based on DenseNet and multi-scale feature fusion. Based on the SSD algorithm, we design the DenseNet-66 module to enhance the feature extraction and utilization capabilities of the model. In the target detection part, a fusion mechanism of multi-scale feature layers is introduced, and an attention feature fusion module is added to further improve the detection performance of the model for small target pedestrians. After training on PASCAL VOC, INRIA, ETH, TUD, CoCo datasets, the experimental results show that our improved SSD model has 300 × 300 input to achieve PASCAL VOC 2007, VOC 2012, INRIA, ETH, TUD, CoCo datasets Up 89.50% mAP, 84.76% mAP, 78.49% mAP, 69.50% mAP, 78.58% mAP and 57.35% mAP. Compared with SSD, the improved SSD detection accuracy increases by 3.75%, 1.77%, 3.06%, 3.66%, 1.90% and 1.87%, respectively.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127741774","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: 1

Lightweight Neural Network-based Web Fingerprinting Model 基于轻量级神经网络的Web指纹识别模型

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00013

Dingyang Liang, Jianing Sun, Yizhi Zhang, Jun Yan

{"title":"Lightweight Neural Network-based Web Fingerprinting Model","authors":"Dingyang Liang, Jianing Sun, Yizhi Zhang, Jun Yan","doi":"10.1109/NaNA56854.2022.00013","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00013","url":null,"abstract":"Onion Routing is an encrypted communication system developed by the U.S. Naval Laboratory that uses existing Internet equipment to communicate anonymously. Miscreants use this means to conduct illegal transactions in the dark web, posing a security risk to citizens and the country. For this means of anonymous communication, website fingerprinting methods have been used in existing studies. These methods often have high overhead and need to run on devices with high performance, which makes the method inflexible. In this paper, we propose a lightweight method to address the high overhead problem that deep learning website fingerprinting methods generally have, so that the method can be applied on common devices while also ensuring accuracy to a certain extent. The proposed method refers to the structure of Inception net, divides the original larger convolutional kernels into smaller ones, and uses group convolution to reduce the website fingerprinting and computation to a certain extent without causing too much negative impact on the accuracy. The method was experimented on the data set collected by Rimmer et al. to ensure the effectiveness.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"22 2 Suppl 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131729451","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

Petri Net Model of MITM Attack Based on NDP Protocol 基于NDP协议的MITM攻击Petri网模型

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00074

Liumei Zhang, Yu Han, Yichuan Wang, Ruiqin Quan

引用次数: 1

Trust Evaluation Model of Social Internet of Things Based on Multi-relationships 基于多关系的社交物联网信任评估模型

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00029

Fan Fan, Hongbin Zhang, Dongmei Zhao, Yanxia Wang, Bin Liu, Jian Liu

引用次数: 0

Spatiotemporal Emotion Recognition Method Based on EEG Signals During Music Listening Using 1D-CNN & Stacked-LSTM 基于1D-CNN和堆叠lstm的音乐听脑电信号时空情感识别方法

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00009

Shengli Liao, Yumei Zhang, Honghong Yang, Xuening Liao

{"title":"Spatiotemporal Emotion Recognition Method Based on EEG Signals During Music Listening Using 1D-CNN & Stacked-LSTM","authors":"Shengli Liao, Yumei Zhang, Honghong Yang, Xuening Liao","doi":"10.1109/NaNA56854.2022.00009","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00009","url":null,"abstract":"Recognizing people's emotions accurately can help to improve people's feeling of happiness by adjusting their emotion immediately, which makes emotion recognition an active research topic recently. Electroencephalography (EEG) signals, which are electrical response of the human brain scalp, reflecting people's emotions and psychological activities, can be applied as an important tool for the emotion recognition. This paper focuses on the emotion recognition based on EEG signals during music listening. To this end, we first propose an emotion recognition scheme by combining the one-dimensional convolutional neural network (1D-CNN) and the stacked long short term memory (Stacked-LSTM), where the 1D-CNN is exploited to extract spatial features from EEG signals automatically and the Stacked-LSTM is applied for further temporal features extraction. We then conducted lots of experiments to validate the efficiency of our proposed scheme regarding the accuracy of emotion recognition. Finally, a comparison between our proposed scheme and other commonly methods used for emotion recognition based EEG signals (e.g., EEGNet, 1D-CNN, LSTM and SVM). The experimental results showed that our proposed scheme is feasible and outperform other commonly used methods in terms of classification accuracy.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133830884","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

QoS Prediction based on the Low-rank Autoregressive Tensor Completion 基于低秩自回归张量补全的QoS预测

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00052

Hong Xia, Qingyi Dong, Yanping Chen, Jiahao Zheng, C. Gao, Zhongmin Wang

{"title":"QoS Prediction based on the Low-rank Autoregressive Tensor Completion","authors":"Hong Xia, Qingyi Dong, Yanping Chen, Jiahao Zheng, C. Gao, Zhongmin Wang","doi":"10.1109/NaNA56854.2022.00052","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00052","url":null,"abstract":"With the rapid development of network services and edge computing, Quality of Service (QoS) has become an important indicator to validate performances of a network. Recommend high-quality services to users based on QoS values. However, the high sparsity of QoS data is because users usually call certain services only at a given time. Missing QoS data is very common in various service recommendation systems. Therefore, it is essential to predict QoS data to accurately recommend high-quality services to users. For the QoS data prediction problem, we build a third-order data tensor “User-Service-Time” for the time series characteristics of QoS data. And introduce time-series variation as a regularization term into third-order tensor Data prediction. We propose a QoS prediction framework using Low-Rank Autoregressive Tensor Completion (LATC). In particular, constructing a third-order tensor data model can better capture the global consistency of the data structure. Time regularization is introduced to take into account the local correlation of the data. Finally, in order to solve the constrained optimization problem, we use the general Alternating Direction Method of Multipliers (ADMM) to minimize the iterative optimization of variables and autoregressive parameters to obtain the final prediction result. Meanwhile, we conduct extensive research experiments on the real dataset WS-Dream. Experiments show that the QoS data prediction accuracy of our proposed QoS prediction method is higher than that of existing prediction methods under different degrees of data density.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124529366","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

DNS Attack Detection Based on Multi-Dimensional Fusion Model 基于多维融合模型的DNS攻击检测

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00021

Yasheng Zhou, Li Yang, Zhixin Wang, G. Li, Xuemei Ning

引用次数: 1

A Novel Least Significant Bit Steganographic Method Based on Hough Transform 一种新的基于霍夫变换的最小有效位隐写方法

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00066

D. Nashat, Loay Mamdouh

{"title":"A Novel Least Significant Bit Steganographic Method Based on Hough Transform","authors":"D. Nashat, Loay Mamdouh","doi":"10.1109/NaNA56854.2022.00066","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00066","url":null,"abstract":"The world becomes a digital era and growth rapidly in technology, so transmission data secretly and securely becomes an essential topic. Many different techniques are available for securing transmission data and using images to embed secret data. Some of these techniques used Hough Transform in their technology. The main goal of these methods is embedding a huge amount of data into images with high level of imperceptibility. However, increasing the amount of embedded data into the image decreasing its quality. Therefore, this work propose a new steganography method based on Least Significant Bit (LSB) using Hough Transform to improve the quality of stego image with increasing the amount of embedded data. The proposed method inverts the LSBs of image pixels to improve the stego image quality. First, we detect edges areas by using improved edge detection filter. Then, we invert LSBs for the pixel in edge area pixels. Finally, the LSBs smooth area pixels of the cover image are inverted. The performance evaluation of the presented method is measured by the stego image quality and the amount of embedded data. The results show that the proposed has better peak signal to noise ratio (PSNR) and capacity in comparison with the current steganography methods.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115576983","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

Feature Matching for Indoor-Oriented Visual Odometry 面向室内视觉里程计的特征匹配

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00050

Xinghui Zhu, Yongzhen Chen, Xiaodong Zhang, Zhiwei Zhang, Baoquan Ren

引用次数: 0

Parallel Algorithm of Geomagnetic Localization in Fire Compartment Based on MapReduce 基于MapReduce的火区地磁定位并行算法

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00065

Wang Tao, Haiyan Zeng, Shenghui Zhao

引用次数: 0