2022 8th International Conference on Web Research (ICWR)最新文献_第3页

DDoS Attacks Detection in Multi-Controller Based Software Defined Network 基于多控制器的软件定义网络DDoS攻击检测

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786246

Parisa Valizadeh, Ahmad Taghinezhad-Niar

引用次数: 2

Improving Random Walk Sampling, Inspired by Two Methods of Choosing Seed Node And No-Retracing With Combination of them with Page Rank Algorithm 基于选择种子节点和无回溯两种方法与页面排序算法相结合的随机行走抽样改进

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786241

Ali Kheradbeygi Moghadam, A. Bastanfard

{"title":"Improving Random Walk Sampling, Inspired by Two Methods of Choosing Seed Node And No-Retracing With Combination of them with Page Rank Algorithm","authors":"Ali Kheradbeygi Moghadam, A. Bastanfard","doi":"10.1109/ICWR54782.2022.9786241","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786241","url":null,"abstract":"One of the algorithms used for sampling complex networks is the classical random walk algorithm, which has been considered due to its good performance. But speed and energy consumption can also be improved by reducing size of input data. In this study, two random walk algorithms inspired by two methods, choosing seed node, and no-retracing algorithm which obtained by changing the classical random walk algorithm, and combining these three algorithms with google page rank algorithm, are discussed. This is done to preserve important nodes and reduce the size of the input data. This sampling was done from the United States flight network database. Also, important characteristics obtained in sampling, such as sampling efficiency, degree distribution, average degree, and average clustering coefficient have been investigated. The algorithms studied in this research each have their own advantages and disadvantages. For example, the no-retracing shows better performance in terms of time and average clustering coefficient. This efficiency is even greater when we use a combination of no-retracing algorithm with google page ranking algorithm. These algorithms can be used when speed is important in decision making, such as deciding on airlines and public transportation, etc. These algorithms are also more energy efficient than the studied algorithms.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129551424","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

Distance Learning: Education Giving Chances and Crises through Modern E-Globalization 远程教育:现代电子全球化下的教育机遇与危机

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786235

Yasser Kareem Al-Rikabi, G. Montazer

引用次数: 0

ExaPPC: a Large-Scale Persian Paraphrase Detection Corpus 大型波斯语释义检测语料库

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786243

Reyhaneh Sadeghi, Hamed Karbasi, Ahmad Akbari

{"title":"ExaPPC: a Large-Scale Persian Paraphrase Detection Corpus","authors":"Reyhaneh Sadeghi, Hamed Karbasi, Ahmad Akbari","doi":"10.1109/ICWR54782.2022.9786243","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786243","url":null,"abstract":"This paper describes the creation of Exa Persian Paraphrase Corpus (ExaPPC), a large paraphrase corpus consisting of monolingual sentence-level paraphrases using different sources. ExaPPC is the first large-scale paraphrase dataset used in Persian paraphrase detection to the best of our knowledge. There are 2.3M labeled sentence pairs in the corpus consisting of a 1M paraphrase label and 1.3M non-paraphrase label. Efforts were made manually and semi-automatically to construct this corpus using techniques such as subtitle alignment, translating existing parallel English-Persian corpus and similarity corpus on English tweets. In addition to enriching the corpus, candidate sentence pairs among tweets have been extracted via NLP tools and labeled by two Persian native speakers. The advantages of this corpus compared to the existing ones are the number of pair sentences, sentence Length variation and textual diversity, including formal and dialogue sentences. The result on the provided test corpus shows that ExaPPC achieves 94% accuracy on paraphrase detection task. The corpus is publicly available11https://github.com/exaco/exappc","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128113875","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

Survey on IoT-based waste Management Systems 基于物联网的废物管理系统研究

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786239

Abolfazl Younesi, Hamed Shahbazi Fard, Alireza Belal Yengikand, Vahid Pezeshki

{"title":"Survey on IoT-based waste Management Systems","authors":"Abolfazl Younesi, Hamed Shahbazi Fard, Alireza Belal Yengikand, Vahid Pezeshki","doi":"10.1109/ICWR54782.2022.9786239","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786239","url":null,"abstract":"The increasing population has led to a disruption of health status due to the waste management system. Waste management in the form of smart cities has created a kind of concern and is an activity that can provide environmental, health, and social benefits. Waste management is a technique to prevent the accumulation of waste. If it is not managed properly, it will lead to a series of unhealthy and unsanitary conditions in the city. Therefore, to prevent and reduce waste it is needed an intelligent waste management system and one of the newest platforms in this basin is the Internet of Things (IoT) platform. The IoT has revolutionized the waste management system. Different governments are trying to meet this great challenge by creating different IoTbased waste management systems and for this purpose different technologies and sensors such as Bluetooth ultrasonic sensors, RFID tags, etc. are used in their architectures to receive real-time information. Due to the benefit of the use of the IoT in waste management, this article analyzes some of the most recent methods for managing and reducing waste based on the Internet of Things, as well as evaluating various barriers, challenges, and solutions. Also, the characteristics of each method and the advantages and disadvantages are stated.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123343914","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

Encrypted Network Traffic Classification Using Deep Learning Method 基于深度学习方法的加密网络流量分类

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786247

Seyedeh Bahareh Banihashemi, Ehsan Aktharkavan

{"title":"Encrypted Network Traffic Classification Using Deep Learning Method","authors":"Seyedeh Bahareh Banihashemi, Ehsan Aktharkavan","doi":"10.1109/ICWR54782.2022.9786247","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786247","url":null,"abstract":"With growing use of internet and online applications, network traffic classification could be much more useful nowadays, because managing network services and quality assurance, two key points in network structure, could be done easily using this kind of classification. Different methods are used for this task, including port-based classification, machine learning and some other algorithms that each of them had its own advantages and disadvantages. For eliminating such disadvantages, deep learning methods are new ways for doing this task due to the power and excellent performance they showed. Furthermore, most of the work done in this field are using non-encrypted traffic or encrypted traffic in mobile networks, but as we know, privacy of data is very important these days. In this article, with the use of deep learning neural network, encrypted traffic of non-mobile data is being classified. For this purpose, we use the UNB ISCX VPN-non-VPN dataset that includes encrypted and unencrypted traffic of different applications. Then we design an algorithm based on DNN that could classify these traffics effectively. Performance of the model was evaluated and 0.86 accuracy and 0.78 fl-score showed that model works well compared to other algorithms used in this area.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130375517","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: 3

A Method for Identifying Personality Traits in Telegram 一种识别电报中人格特征的方法

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786253

M. Shayegan, Mohaddese Valizadeh

引用次数: 2

Detection of Multiple Emotions in Texts Using Long Short-Term Memory Recurrent Neural Networks 使用长短期记忆递归神经网络检测文本中的多种情绪

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786225

Sepideh Saeedi Majd, Habib Izadkhah, S. Lotfi

引用次数: 0

Indirect Website Evaluation: Currently Available Tools 间接网站评估:当前可用的工具

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786252

Hamidreza Saeidnia, Marcin Kozak, Sara Saeidnia

引用次数: 2

Provide an Improved Model for Detecting Persian SMS Spam by Integrating Deep Learning and Machine Learning Models 结合深度学习和机器学习模型，提出一种改进的波斯语垃圾短信检测模型

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786238

Roya Khorashadizadeh, S. J. Jassbi, Alireza Yari

{"title":"Provide an Improved Model for Detecting Persian SMS Spam by Integrating Deep Learning and Machine Learning Models","authors":"Roya Khorashadizadeh, S. J. Jassbi, Alireza Yari","doi":"10.1109/ICWR54782.2022.9786238","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786238","url":null,"abstract":"Spam is an example of unwanted content sent by unknown users and causing problems for mobile phone users. Disadvantages of spam include the inconvenience to the user, the loss of network traffic, the imposition of a calculation fee, the occupation of the physical space of the mobile phone, the misuse and fraud of the recipient. For this reason, the automatic detection of annoying text messages can be fundamental. Also, recognizing intelligently generated text messages is a challenge. Nevertheless, the current methods in this field face obstacles, such as the lack of appropriate Persian datasets. Experiences have shown that approaches based on deep and combined learning have better results in uncovering the annoying text messages. Accordingly, this study has attempted to provide an efficient method for detecting SMS spam by integrating machine learning classification algorithms and deep learning models. In the proposed method, after performing preprocessing on our collected dataset, two convolutional neural network layers and one LSTM layer and a fully connected layer are applied to extract the features are applied on the data which forms the deep learning part of the proposed method. The Support vector machine then utilizes the extracted information and features to perform the final classification, which is a part of the Machine Learning methods. The results show that the proposed model implements better than other algorithms and 97. 7% accuracy was achieved.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114704255","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