2021 7th International Conference on Web Research (ICWR)最新文献_第2页

Towards a Novel Framework for Trust Driven Web URL Recommendation Incorporating Semantic Alignment and Recurrent Neural Network 基于语义对齐和递归神经网络的信任驱动Web URL推荐框架研究

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443136

K. N, G. Deepak

{"title":"Towards a Novel Framework for Trust Driven Web URL Recommendation Incorporating Semantic Alignment and Recurrent Neural Network","authors":"K. N, G. Deepak","doi":"10.1109/ICWR51868.2021.9443136","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443136","url":null,"abstract":"Recommendation System plays an important role in delivering relevant data to the user. A recommender system is also used to display relevant websites with respect to the user query. As the amount of malicious web pages in World Wide Web is quite enormous, there is a huge probability that the URL might be harmful to the user. This paper proposes a Trust-based URL recommendation technique using Semantic Alignment driven Knowledge aggregation methodology along with Artificial neural network and Glowworm Swarm Optimization. The data used for training the Recurrent Neural Network is the URL Trees formulated from the dataset combined with data after classification from fact-checkers, which is later used to check the Threat level of the URL from the initial solution set. Based on this index, the URL is recommended in such a way that the URL is more relevant and Threat is minimized. The architecture’s performance is calculated and compared with the baseline approaches and it is clearly observed that the proposed trust-based URL recommendation system is dominating in terms of performance and attained a precision and accuracy of 96.84% and 95.87% respectively.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121923659","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

Toward Stopping Incel Rebellion: Detecting Incels in Social Media Using Sentiment Analysis 制止叛逆:利用情感分析在社交媒体上发现叛逆

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443027

Mohammad Hajarian, Zahra Khanbabaloo

{"title":"Toward Stopping Incel Rebellion: Detecting Incels in Social Media Using Sentiment Analysis","authors":"Mohammad Hajarian, Zahra Khanbabaloo","doi":"10.1109/ICWR51868.2021.9443027","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443027","url":null,"abstract":"Incels, which stand for involuntary celibates, refer to online community members who identify themselves as individuals that women are not attracted to them. They are usually involved in misogyny and hateful conversations on social networks, leading to several terrorist attacks in recent years, also known as incel rebellion. In order to stop terrorist acts like this, the first step is to detect incels members in social networks. To this end, user-generated data can give us insights. In previous attempts to identifying incels in social media, users’ likes and fuzzy likes data were considered. However, another piece of information that can be helpful to identify such social network members is users’ comments. In this study, for the first time, we have considered users’ comments to identify incels in the social networks. Accordingly, an algorithm using sentiment analysis was proposed. Study results show that by implementing the proposed method on social media users’ comments, incel members can be identified in social networks with an accuracy of 78.8%, which outperforms the previous work in this field by 10.05%.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123157797","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

Increase or Decrease Network Robustness with Genetic algorithms : A method for maximization or minimization of network robustness in attack or random failure scenarios 利用遗传算法增加或减少网络鲁棒性:一种在攻击或随机故障情况下最大化或最小化网络鲁棒性的方法

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443130

Manouchehr Rasouli, A. Kamandi

{"title":"Increase or Decrease Network Robustness with Genetic algorithms : A method for maximization or minimization of network robustness in attack or random failure scenarios","authors":"Manouchehr Rasouli, A. Kamandi","doi":"10.1109/ICWR51868.2021.9443130","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443130","url":null,"abstract":"Network robustness is a fundamental measure for finding the network tolerance against failures and attacks. There are many methods to measure network robustness for a variety of networks like a network of routers, transportation network and so on. Increasing network robustness against failures and attacks is a fundamental problem which many methods have been introduced like random preferential node attachment, random link attachment and etc. In this paper, we present a method to increase the attack impact or decrease random failure impact on the network depending on the purpose. Following method uses genetic algorithm as an optimization approach for improving network robustness measurement function. In order to do that, we start to find a sequence of node removals which have the greatest impact on the robustness measurement function. In case of increasing the network robustness, we duplicate the aforementioned nodes. This sequence can also serve us as a guidance for attacking harmful networks, like fire or disease distribution with minimal cost.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131495007","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

Android Malware Detection and Classification Based on Network Traffic Using Deep Learning 基于网络流量的深度学习Android恶意软件检测与分类

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443025

M. Gohari, S. Hashemi, Lida Abdi

{"title":"Android Malware Detection and Classification Based on Network Traffic Using Deep Learning","authors":"M. Gohari, S. Hashemi, Lida Abdi","doi":"10.1109/ICWR51868.2021.9443025","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443025","url":null,"abstract":"Users of smartphones in the world has grown significantly, and attacks against these devices have increased. Many protection techniques for android malware detection have been proposed; however, most of them lack the early detection of malware. Hence, there is an intense need before to expand a mechanism to identify malicious programs before utilizing the data. Moreover, achieving high accuracy in detecting Android malware traffic is another critical problem. This research proposes a deep learning framework using network traffic features to detect Android malware. Commonly, machine learning algorithms need data preprocessing, but these preprocessing phases are time- consuming. Deep learning techniques remove the need for data preprocessing, and they perform well on malware detection problems. We extract local features from network flows by using the one-dimensional CNN and employ LSTM to detect the sequential relationship between the considerable features. We utilize a real-world dataset CICAndMal2017 with network traffic features to identify Android malware. Our model achieves the accuracy of 99.79, 98.90%, and 97.29%, respectively, in binary, category, and family classifications scenarios.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120921641","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: 12

Evaluation and Analysis of IoT-based Smart City Design Parameters 基于物联网的智慧城市设计参数评价与分析

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443114

Reza Pourmohammadhosein Niaky, Parastoo Vakili Tahami

{"title":"Evaluation and Analysis of IoT-based Smart City Design Parameters","authors":"Reza Pourmohammadhosein Niaky, Parastoo Vakili Tahami","doi":"10.1109/ICWR51868.2021.9443114","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443114","url":null,"abstract":"The smart city is a framework created primarily from information and communication technology to develop, expand, and promote sustainable development practices to address the growing challenges of urbanization. Smart city development is one of the most challenging issues in recent years. The smart city as new technology has attracted various sectors, including the economy and urban services. Smart cities as new technology have attracted the attention of various industry sectors, economy, transportation, urban services, and investment. One of the challenges facing smart city studies is that, after nearly a decade, a comprehensive evaluation plan for smart cities has not been presented. Through the studies, criteria have been provided to provide a suitable evaluation plan. In this article, evaluation strategies are presented. Also, the analysis of the most effective criteria for presenting a smart city plan using IoT technology and providing a comprehensive view of the smart city, the required security, and its efficient architecture is presented. With modern communications, the loss of costs and economic capital is prevented in the smart city, and human resources are used optimally. The smart city is complex systematic engineering. This requires planning, construction, management, and implementation and evaluation, optimization, and regulation to build a smart city in a forward-looking, logical, and effective way. The challenges ahead for smartening can be divided into categories that will be examined in detail. Finally, a general conclusion is presented from all studies that the only purpose of the intelligent industry is to improve living standards.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"646 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116479571","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

Identifying High-Quality User Replies Using Deep Neural Networks 使用深度神经网络识别高质量的用户回复

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443143

Masoumeh Rajabi, Mohammad Ehsan Basiri, Shahla Nemati

{"title":"Identifying High-Quality User Replies Using Deep Neural Networks","authors":"Masoumeh Rajabi, Mohammad Ehsan Basiri, Shahla Nemati","doi":"10.1109/ICWR51868.2021.9443143","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443143","url":null,"abstract":"With the significant expansion of Q&A forums and the increasing need for users to access useful information, identifying quality content in text forums is of particular importance. Previous studies have focused on extracting several types of quality features from text that may be a time and labor-intensive task. To address this problem, in this paper, a long short-term memory (LSTM) deep neural network model is proposed to determine high-quality responses of users in text forums using only raw text of user replies. In the proposed model, embeddings from language models (ELMo) are usesd to represent words in vectors or embeddings. The proposed model is evaluated on two datasets: The TripAdvisor for New York City (NYC) and the Ubuntu Linux distribution online forums. Comparison of the results obtained using the proposed model and support vector machines (SVM), linear regression (LR), artificial neural networks (ANN), and naïve Bayes (NB) algorithms showed that, using only textual features, the accuracy of the proposed model was 43% and 28% higher compared to the highest accuracy obtained by the four traditional machine learning (ML) algorithms on the NYC and the Ubuntu datasets, respectively. This improvement was about 17% and 16% compared to the best results obtained by ML algorithms using both textual and quality dimension features.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128868819","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

Bibliometric of Semantic Enrichment 语义丰富的文献计量学

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443147

M. Shayegan, Mohammad Mehdi Mohammad

引用次数: 0

An Unsupervised Feature Selection for Web Phishing Data using an Evolutionary Approach 基于进化方法的网络钓鱼数据无监督特征选择

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443148

Motahare Akhavan, Seyed Mohammad Hossein Hasheminejad

{"title":"An Unsupervised Feature Selection for Web Phishing Data using an Evolutionary Approach","authors":"Motahare Akhavan, Seyed Mohammad Hossein Hasheminejad","doi":"10.1109/ICWR51868.2021.9443148","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443148","url":null,"abstract":"Phishing is one of the most serious cybercrimes used by fraudsters to steal individuals and organizations' identities and financial information. The most common form of phishing is phishing through fake websites. In recent years, phishing detection methods based on machine learning have gained attention due to their high accuracy. Feature selection is a preprocessing step in data mining and machine learning that is used to reduce the size of the feature space and find significant features while achieving comparable or higher accuracy. In this paper, an unsupervised feature selection method, called LAPPSO, is proposed for web phishing data. To find the most informative features, LAPPSO applies an improved version of PSO with a greater exploration for improving the global search and also uses the Laplacian score for local search. Based on experimental results obtained from applying LAPPSO on two well-known phishing datasets, our algorithm achieves the average F-measure of 96% while reducing the number of the features significantly. Moreover, the training time of the learning model is reduced to almost half using the selected features.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"2 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130044577","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

Optimization of Average Travel Time of Passengers in Tehran Metro Using Data Mining Methods 基于数据挖掘方法的德黑兰地铁乘客平均出行时间优化

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443110

E. Rezaei, H. Rahmani, Elham Ashraf

{"title":"Optimization of Average Travel Time of Passengers in Tehran Metro Using Data Mining Methods","authors":"E. Rezaei, H. Rahmani, Elham Ashraf","doi":"10.1109/ICWR51868.2021.9443110","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443110","url":null,"abstract":"In the design and development of public transportation systems such as urban railways, not only the route and location of stations, but also the timetable of fleet movements must be considered. Train timetables are an important factor as they influence customer satisfaction, metro operating costs, and environmental health; as a result, train timetables optimization increases the service quality. In this timetable optimization, train stopping time at the station as well as passenger waiting time would be taken into account. In time optimization research, mathematical analysis and simulation of data mining algorithms have been used so far for general changes in timetables. In this article, the data are examined in detail in order to find significant differences with other data. In this paper, the data of delayed trips in Tehran metro have been examined using data mining analysis methods. The Discriminant Analysis method has been used to identify delayed trips with significant differences after a relative understanding of important features of the dataset. Considering the power of the genetic algorithm to achieve an optimal solution, the approach proposed in this paper is to provide a solution to combine this algorithm and discriminant analysis method. The result of this paper is 40 final chromosomes, indicating trips that exhibit a significant difference in latency, compared to other trips. And according to the characteristics of these trips, the optimization can be done by changes in the timetable.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443789","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

An Approach to Improve Apriori Algorithm for Extraction of Frequent Itemsets 一种改进Apriori算法的频繁项集提取方法

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443137

Mohammad Javad Shayegan, Parsa Asgari Namin

引用次数: 1