2022 International Conference on Networking and Network Applications (NaNA)最新文献_第7页

User-centric Service System for Network beyond IMT-2020 (5G) 超越IMT-2020 (5G)网络以用户为中心的服务体系

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00047

Zhan Liu, Xiaojie Zhu, Peng Li, Jinlan Ma, Xiaozhi Yuan, Yuxiang Jiang, Qingyang Wang

引用次数: 0

On IRS-Aided Covert Communications of D2D-Enabled Cellular Networks 支持d2d的蜂窝网络的irs辅助隐蔽通信

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00023

Yihuai Yang, Shikai Shen, Yumei She, Wu Wang, Bin Yang, Yangshui Gao

引用次数: 0

Self-Anomaly-Detection Model Training via Initialized Meta Model 基于初始化元模型的自异常检测模型训练

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00087

Xindi Ma, Cunzhu Su, Jianfeng Ma, Qi Jiang, Ning Xi, Sheng Gao, Kang Xie

{"title":"Self-Anomaly-Detection Model Training via Initialized Meta Model","authors":"Xindi Ma, Cunzhu Su, Jianfeng Ma, Qi Jiang, Ning Xi, Sheng Gao, Kang Xie","doi":"10.1109/NaNA56854.2022.00087","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00087","url":null,"abstract":"Anomaly detection has become a key challenge affecting the training accuracy of machine learning. Because the training data is usually collected from Internet, many noised samples will be captured and these samples can decrease the model training accuracy. However, because the abnormal samples are difficult to predict when the samples are collected, and the training samples collected may contain many unknown exception categories, and the labels of normal samples may be incorrect, in this case, it is difficult to train an anomaly detection model based on supervised learning to accurately identify the anomaly samples. In this paper, we propose a new unsupervised anomaly detection method based on BiGAN, namely Rt-BiGAN, to identify the outliers in the training data. Firstly, we propose a Bigan network initialization method based on meta-learning algorithm with a small number of normal samples. Then, a self-supervised drop training is designed to improve the detection ability of the model. Finally, we evaluate our Rt-BiGAN over real-world datasets and the simulations results demonstrate that our mechanism is effective to detect the outliers in model training data.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"9 13 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":"117349506","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 Game Theory Perspective on TCP Congestion Control Evaluation 基于博弈论的TCP拥塞控制评估

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00039

Pin Chen, Naijie Gu, Daxing Liu, Qianqian Yu

引用次数: 0

EA-based Mitigation of Hardware Trojan Attacks in NoC of Coarse-Grained Reconfigurable Arrays 基于ea的粗粒度可重构阵列NoC硬件木马攻击缓解

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00097

Zeyu Li, Junjie Wang, Zhao Huang, Quan Wang

引用次数: 0

Class Continuous LOD Algorithm for Lightweight WebGL Rendering Optimization 轻量级WebGL渲染优化的类连续LOD算法

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00090

Jiandong Wang, Xiang Xia, Zhiwei Zhang, Yuanyu Zhang, Yulong Shen, Baoquan Ren

{"title":"Class Continuous LOD Algorithm for Lightweight WebGL Rendering Optimization","authors":"Jiandong Wang, Xiang Xia, Zhiwei Zhang, Yuanyu Zhang, Yulong Shen, Baoquan Ren","doi":"10.1109/NaNA56854.2022.00090","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00090","url":null,"abstract":"WebGL is one of the most promising implementations of 3D visualization and can be accessed across platforms. However, rendering a large scene using WebGL requires a lot of computational overhead because every vertex of the model in the scene needs to be calculated. Traditional WebGL rendering solutions include discrete and continuous Levels of Detail (LOD) algorithms. Discrete algorithms display models with different details in segments, causing the problem of model mutation. Although continuous algorithms can generate models with similar details to solve the problem of model mutation, the model distortion problem is introduced when the model details are low. In this paper, a Class Continuous LOD (CCLOD) algorithm for lightweight rendering is proposed, which dynamically adjusts model details according to the visual distance, and reduces the computational overhead of rendering by reducing model details while keeping consistent visual effect. In order to solve the problem of model mutation and distortion in the LOD algorithm, the CCLOD algorithm puts forward relevant constraints on the process of adjusting model details. According to these constraints, a collapse proportion generation algorithm is proposed. The collapse proportion is generated by using the distance between objects, and the low-cost triangles of the model are deleted according to the collapse proportion to generate a model with continuous details. It makes the transition between multi-level models smoother to avoid model mutation and limits the max-imum collapse ratio to avoid model distortion. Experimental results show that compared with the traditional LOD algorithm, the CCLOD algorithm reduces the number of triangles by 12.66% and the video memory usage by 17.5%.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"283 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":"133014446","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

Network Security Situation Prediction Implemented by Attention and BiLSTM 基于注意力和BiLSTM的网络安全态势预测

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00043

Dongmei Zhao, Yaxing Wu, Qingru Li

{"title":"Network Security Situation Prediction Implemented by Attention and BiLSTM","authors":"Dongmei Zhao, Yaxing Wu, Qingru Li","doi":"10.1109/NaNA56854.2022.00043","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00043","url":null,"abstract":"With the increasing diversification and complexity of network security attacks, it is becoming more and more difficult to predict the network situation. In order to improve the effect of situation prediction, this paper constructs a network security situation prediction model for a Improved Particle Swarm Optimization and Attention fusion Bidirectional Long Short-Term Memory (IPSO-ABiLSTM). First, there is no real situation value for the UNSW-NB15 data set, and a situation value is generated based on the impact of the attack. Secondly, the particle swarm algorithm is improved. The IPSO algorithm makes the algorithm's global and local search capabilities more balanced and faster to converge. Finally, optimizing the hyperparameters of the BiLSTM network fused with the attention mechanism to obtain the final model, and combined with PSO-BiLSTM network, PSO-LSTM network, BiLSTM model for performance comparison. The experimental results show that the IPSO-ABiLSTM in this paper has a fitting degree of up to 0.9922, and the error value is relatively smaller, which verifies the effectiveness of the model proposed in this paper in the network security situation prediction problem.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"27 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":"133771206","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

Blind Identification of Channel Codes under AWGN and Fading Conditions via Deep Learning 基于深度学习的AWGN和衰落条件下信道码盲识别

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00020

Haifeng Peng, Chunjie Cao, Yang Sun, Haoran Li, Xiuhua Wen

{"title":"Blind Identification of Channel Codes under AWGN and Fading Conditions via Deep Learning","authors":"Haifeng Peng, Chunjie Cao, Yang Sun, Haoran Li, Xiuhua Wen","doi":"10.1109/NaNA56854.2022.00020","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00020","url":null,"abstract":"Blind identification of channel codes is crucial in intelligent communication and non-cooperative signal processing, and it plays a significant role in wireless physical layer security, information interception, and information confrontation. Previous researches show a high computation complexity by manual feature extractions, in addition, problems of indisposed accuracy and poor robustness are to be resolved in a low signal-to-noise ratio (SNR). For solving these difficulties, based on deep residual shrinkage network (DRSN), this paper proposes a novel recognizer by deep learning technologies to blindly distinguish the type and the parameter of channel codes without any prior knowledge or channel state, furthermore, feature extractions by the neural network from codewords can avoid intricate calculations. We evaluated the performance of this recognizer in AWGN, single-path fading, and multi-path fading channels, the results of the experiments showed that the method we proposed worked well. It could achieve over 85 % of recognition accuracy for channel codes in AWGN channels when SNR is not lower than 4dB, and provide an improvement of more than 5% over the previous research in recognition accuracy, which proves the validation of the proposed method.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"173 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":"123255618","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

Optimal Location Design for UAV Covert Communications with a Full-Duplex Receiver 无人机全双工隐蔽通信的最优定位设计

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00014

Zewei Guo, Shuangrui Zhao, Jiandong Wang, Haipeng Lit, Yulong Shen

引用次数: 2

Lightweight and Efficient Distributed Cooperative Intrusion Detection System for Intelligent Swarms 面向智能蜂群的轻量级高效分布式协同入侵检测系统

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00048

Zhaoyang Li, Zhiwei Zhang, Zehan Chen, Hao Duan, Hongjun Li, Baoquan Ren

{"title":"Lightweight and Efficient Distributed Cooperative Intrusion Detection System for Intelligent Swarms","authors":"Zhaoyang Li, Zhiwei Zhang, Zehan Chen, Hao Duan, Hongjun Li, Baoquan Ren","doi":"10.1109/NaNA56854.2022.00048","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00048","url":null,"abstract":"Intrusion Detection Systems (IDSs) have been wildly used in various environments to actively detect internal and external attacks with high accuracy. Unfortunately, the traditional IDSs cannot distinguish new or unknown attacks from abnormal behaviors effectively, and that makes them infeasible to protect the emerging dynamic open information systems. Subsequently, artificial intelligence (AI) algorithms are introduced into IDSs to support the recognization of unexplored malicious behaviors. However, most of the existing AI-driven IDSs are not able to be directly applied to intelligent swarm scenarios, which are typically employed to aggregate heterogeneous or homogeneous elements (e.g., autonomous vehicles, drones) to solve complex problems that the individual members cannot deal with, due to the characteristics of mobility and complexity of intelligent elements. Therefore, in this paper, we propose a lightweight and efficient distributed cooperative IDS (DCIDS) for intelligent swarms. On one hand, to efficiently detect the malicious behaviors among swarm elements, we design a collaborative detection model which utilizes multi-dimension features including the swarm elements' position, storage-computing resource consuming levels, network traffics, et al. On the other hand, to predict the movement trends and detect attacks of resource-limited swarm elements, we construct a concrete DCIDS scheme by employing the Kalyan Filter algorithm and Long Short Term Memory Network (LSTM) algorithm. Furthermore, our experimental results demonstrate that the proposed DCIDS scheme outperforms the previous IDS schemes on attack detection/classification accuracy and efficiency in intelligent swarm environments and also achieves an accuracy of 98.00%.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"16 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":"127121680","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