2020 6th International Conference on Web Research (ICWR)最新文献_第3页

A New Method for Intrusion Detection on RPL Routing Protocol Using Fuzzy Logic 基于模糊逻辑的RPL路由协议入侵检测新方法

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122278

Behnam Farzaneh, Mohammad Koosha, Elahe Boochanpour, Emad Alizadeh

引用次数: 16

A New Metric to Evaluate Network Robustness 一种评价网络鲁棒性的新指标

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122284

Maliheh Ghomsheh, A. Kamandi

引用次数: 3

Coronavirus Spreading Analysis Using Dynamic Spreading Factor Epidemic Models 基于动态传播因子流行模型的冠状病毒传播分析

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122308

Zahra Farahi, A. Kamandi

引用次数: 0

Personalization of E-Learning Environment Using the Kolb's Learning Style Model 基于Kolb学习风格模型的网络学习环境个性化

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122314

Tahereh Sanjabi, G. Montazer

引用次数: 8

A Game Theory-based Mechanism to Optimize the Traffic Congestion in VANETs 基于博弈论的vanet交通拥堵优化机制

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122324

Khalilollah Raeisi Lejjy, Esmaeil Amiri, Emad Alizadeh, Mohammad Hossein Rezvani

引用次数: 1

Hierarchical Three-module Method of Text Classification in Web Big Data Web大数据文本分类的分层三模块方法

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122326

Zahra Rezaei, B. Eslami, M. Amini, Mohammad Eslami

{"title":"Hierarchical Three-module Method of Text Classification in Web Big Data","authors":"Zahra Rezaei, B. Eslami, M. Amini, Mohammad Eslami","doi":"10.1109/ICWR49608.2020.9122326","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122326","url":null,"abstract":"Text analysis is a method for extracting knowledge from text. Memory and time limitations in processing big data is crucial due to data sources distributed in web, search engines and socials network sites. In addition, due to automatizing search process, summarizing and finding the interests of users, immediate classification of various texts in a streaming manner has gained attention in industrial and scientific fields. Hierarchical classification of text is among common issues which is simply possible in traditional methods using bag of words; however, while talking about big data and when there are a lot of labels of classes, employing traditional methods will not meet the needs of societies. With the improvement of data in internet and social networks, more powerful methods are needed which can classify the data closely and immediately. Through abstraction in textual data, deep learning can deal with these challenges. In this paper a deep learning method will be introduced which is based on hierarchical classification (HAN) named HAN-MODI and which can classify texts from social networks and web sites with an accuracy of 98.81% at the real time bilingually in English and Farsi. This paper also shows that this complex network with three modules word, sentence and document can work better at word level and there is no need to know syntactic or semantics structure of language. The novelty of the proposed method is adding a third level to the hierarchical structure for general detection and for more exact detection of the class. In addition, classification using this method will be multi-level classification and finally with a change in HAN, this method can be used with Farsi texts. Model improvement is done by adding a new layer above the architecture HAN. We called it as segmentation of sentences into expressions Bag of Sentences and added a dynamicity window in any stage that applied attention mechanism simultaneously.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"61 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113977568","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: 2

SW-DBSCAN: A Grid-based DBSCAN Algorithm for Large Datasets SW-DBSCAN:基于网格的大数据集DBSCAN算法

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122313

Negar Ohadi, A. Kamandi, M. Shabankhah, Seyed Mohsen Fatemi, S. Hosseini, Alireza Mahmoudi

引用次数: 16

An Efficient Ensemble of Convolutional Deep Steganalysis Based on Clustering 基于聚类的卷积深度隐写分析的高效集成

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122294

Tayebe Abazar, Peyman Masjedi, M. Taheri

引用次数: 4

An Efficient Deep Learning Method for Encrypted Traffic Classification on the Web 一种高效的Web加密流量分类深度学习方法

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122299

Shiva Soleymanpour, H. Sadr, H. Beheshti

{"title":"An Efficient Deep Learning Method for Encrypted Traffic Classification on the Web","authors":"Shiva Soleymanpour, H. Sadr, H. Beheshti","doi":"10.1109/ICWR49608.2020.9122299","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122299","url":null,"abstract":"Traffic classification plays an important role in network management and cyber-security. With the development of the Internet, online applications and in the following encrypted techniques, encrypted traffic has changed to a major challenge for traffic classification. In fact, unbalanced data, in which the unbalanced distribution of samples across classes lead to the classification performance reduction, is considered as one of the prominent challenges in encrypted traffic classification. Although previous studies tried to deal with the class imbalance problem in the pre-processing step using machine learning and particularly deep learning models, they are still confronting with some limitations. In this regard, a new classification method is proposed in this paper that tries to deal with the problem of unbalanced data during the training process. The proposed method employs a cost-sensitive convolution neural network and considers a cost for each classification according to the distribution of classes. These costs are then applied to the network along the training process to enhance the overall accuracy. Based on the empirical results, the proposed model obtained higher classification performance (about 2% on average) compared to the Deep Packet method.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124937154","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: 11

Machine Translation Using Improved Attention-based Transformer with Hybrid Input 基于混合输入的改进注意力转换器的机器翻译

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122317

M. Abrishami, Mohammad J. Rashti, M. Naderan

{"title":"Machine Translation Using Improved Attention-based Transformer with Hybrid Input","authors":"M. Abrishami, Mohammad J. Rashti, M. Naderan","doi":"10.1109/ICWR49608.2020.9122317","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122317","url":null,"abstract":"Machine Translation (MT) refers to the automated software-based translation of natural language text. The embedded complexities and incompatibilities of natural languages have made MT a daunting task facing numerous challenges, especially when it is to be compared to a manual translation. With the emergence of deep-learning AI approaches, the Neural Machine Translation (NMT) has pushed MT results closer to human expectations. One of the newest deep learning approaches is the sequence-to-sequence approach based on Recurrent Neural Networks (RNN), complex convolutions, and transformers, and employing encoders/decoder pairs. In this study, an attention-based deep learning architecture is proposed for MT, with all layers focused exclusively on multi-head attention and based on a transformer that includes multi-layer encoders/decoders. The main contributions of the proposed model lie in the weighted combination of layers' primary input and output of the previous layers, feeding into the next layer. This mechanism results in a more accurate transformation compared to non-hybrid inputs. The model is evaluated using two datasets for German/English translation, the WMT'14 dataset for training, and the newstest'2012 dataset for testing. The experiments are run on GPD-equipped Google Colab instances and the results show an accuracy of 36.7 BLEU, a 5% improvement over the previous work without the hybrid-input technique.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131618524","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