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

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

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

PEDM: Pre-Ensemble Decision Making for Malware Identification and Web Files PEDM:恶意软件识别和网络文件的预集成决策

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122322

Elham Velayati, Seyed Mehdi Hazrati Fard

{"title":"PEDM: Pre-Ensemble Decision Making for Malware Identification and Web Files","authors":"Elham Velayati, Seyed Mehdi Hazrati Fard","doi":"10.1109/ICWR49608.2020.9122322","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122322","url":null,"abstract":"Connecting your system or device to an insecure network can create the possibility of infecting by the unwanted files. Malware is every malicious code that has the potential to harm any computer or network. So, detecting harmful files is a crucial duty and an important role in any system. Machine learning approaches use a variety of features such as Opcodes, Bytecodes, and System-calls to achieve accurate malware identification. Each of these feature sets provides a unique semantic view, while, considering the effect of altogether is more reliable to detect attacks. Malware can disguise itself in some views, but hiding in all views will be much more difficult. Multi-View Learning (MVL) is an outstanding approach that considers multiple views of a problem to improve the overall performance. In this paper, inspiring MVL an approach is proposed to incorporate some various feature sets and exploit complementary information to identify a file. In this way, the consensus of multiple views is used to minimize the overall error of a classifier based on sparse representation. To show the generalization power of the proposed method, various datasets are employed. Experimental results indicate that in addition to high performance, the proposed method has the advantage of overcoming the imbalanced conditions.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"17 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":"132195058","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

A List-based Heuristic Algorithm for Static Task Scheduling in Heterogeneous Distributed Computing Systems 异构分布式计算系统中静态任务调度的启发式算法

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122306

Hadi Gholami, Reza Zakerian

{"title":"A List-based Heuristic Algorithm for Static Task Scheduling in Heterogeneous Distributed Computing Systems","authors":"Hadi Gholami, Reza Zakerian","doi":"10.1109/ICWR49608.2020.9122306","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122306","url":null,"abstract":"Executing complicated computations in parallel increases the speed of computing and brings user delight to the system. Decomposing the program into several small programs and running multiple parallel processors are modeled by Directed Acyclic Graph. Scheduling nodes to execute this task graph is an important problem that will speed up computations. Since task scheduling in this graph belongs to NP-hard problems, various algorithms were developed for node scheduling to contribute to quality service delivery. The present study brought a heuristic algorithm named looking ahead sequencing algorithm (LASA) to cope with static scheduling in heterogeneous distributed computing systems with the intention of minimizing the schedule length of the user application. In the algorithm proposed here, looking ahead is considered as a criterion for prioritizing tasks. Also, a property called Emphasized Processor has been added to the algorithm to emphasize the task execution on a particular processor. The effectiveness of the algorithm was shown on few workflow type applications and the results of the algorithm implementation were compared with two more heuristic and meta-heuristic algorithms.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"1 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":"130346138","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: 6