2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)最新文献

{"title":"Immunizer: A Scalable Loosely-Coupled Self-Protecting Software Framework using Adaptive Microagents and Parallelized Microservices","authors":"O. Iraqi, H. Bakkali","doi":"10.1109/WETICE49692.2020.00013","DOIUrl":"https://doi.org/10.1109/WETICE49692.2020.00013","url":null,"abstract":"IT professionals are overwhelmed by rapidly-changing technology and growing complexity. Additional challenges are introduced by cyber-security. Self-protecting software tries to alleviate this situation by combining principles and techniques from both autonomic computing and software security. However, this combination creates scalability issues, as well as cross-cutting concerns. In this work, we present Immunizer: A Scalable Loosely-Coupled Self-Protecting Software Framework. Immunizer extends our Application-level Unsupervised Outlier-based Intrusion Detection and Prevention Framework by leveraging the architectural building blocks of autonomic computing, and adopting a microagent/microservice architectural model, augmented with distributed cluster computing, for maximum scalability and separation of concerns. More specifically, we design each of the Monitor, Analyze, Plan and Execute functions of the autonomic MAPE-K control loop as a parallelized microservice, while we model its Knowledge function as a data streaming, caching and storage infrastructure. Moreover, we design the Sensor and Effector touchpoint modules as adaptive lightweight runtime application instrumentation microagents.","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128504630","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}

{"title":"Methods for effective and reliable resource allocation in service-oriented systems","authors":"Lukasz Falas, Patryk Schauer","doi":"10.1109/WETICE49692.2020.00033","DOIUrl":"https://doi.org/10.1109/WETICE49692.2020.00033","url":null,"abstract":"Many life and infrastructure critical service-oriented systems require rigid compliance of their execution parameters values with the defined Service Level Agreement (SLA). In such systems reliable and proper execution of provided functionalities is an important part of their security assurance procedures. To meet these demands, the majority of system architects overprovision systems’ infrastructures to ensure proper operability even during high load scenarios. In this article, a proposition of methods for effective and reliable resource allocation methods for service-oriented systems is proposed. The main foundation of this research lays in the user request processing procedure. This paper proposes a just-intime resource provisioning procedure for composite service with optimization methods ensuring proper resource allocation, compliance with the defined SLA and optimization of composite service delivery cost. The paper describes the concept of such request processing procedure, proposition of resource allocation methods and their experimental verification results.","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134305882","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}

{"title":"A Spam Email Detection Mechanism for English Language Text Emails Using Deep Learning Approach","authors":"S. Kaddoura, O. Alfandi, Nadia Dahmani","doi":"10.1109/WETICE49692.2020.00045","DOIUrl":"https://doi.org/10.1109/WETICE49692.2020.00045","url":null,"abstract":"Phishing emails are emails that pretend to be from a trusted company that target users to provide personal or financial information. Sometimes, they include links that may download malicious software on user’s computers, when clicked. Such emails are easily detected by spam filters that classify any email with a link as a phishing email. However, emails that have no links, link-less emails, requires more effort from the spam filters. Although many researches have been done on this topic, spam filters are still classifying some benign emails as phishing and vice-versa. This paper is focused on classifying link-less emails using machine learning approach, deep neural networks. Deep neural networks differs from simple neural network by having multiple hidden layers where data must be processed before reaching the output layer. The data used in this research is publicly available online. Hyper parameter optimization, was performed, using different settings on the data. In order to demonstrate the effectiveness of the approach, precision, recall and accuracy were computed. The results show that the deep neural network performed well in many of its settings.","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129241018","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}

{"title":"Copyright","authors":"","doi":"10.1109/wetice49692.2020.00003","DOIUrl":"https://doi.org/10.1109/wetice49692.2020.00003","url":null,"abstract":"","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131827650","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}

{"title":"RFMC: a spending-category segmentation","authors":"Sahar Allegue, T. Abdellatif, Khalil Bannour","doi":"10.1109/WETICE49692.2020.00040","DOIUrl":"https://doi.org/10.1109/WETICE49692.2020.00040","url":null,"abstract":"With regards to an exceptionally competitive financial market impacted by legislative changes and with the evolution of customer’s behavior, banks must provide customer-centric assistance, services and products. To understand their customers, segmentation is a classical technique where banks classify their customers following well-defined banking rules or customer profiles (RFM). In this paper, we propose a novel segmentation, called RFMC that better represents customers’ behavior by using, not only customers’ profiles but also a categorization of their spending. We show that, compared to the classical RFM-based segmentation, RFMC allows for advanced services that better fulfill customer’s wishes, expectations and needs, by leveraging offers that fit their spending behaviors.","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127276914","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}

{"title":"An Unsupervised Feature Selection Method for Data-Driven Anomaly Detection Systems","authors":"N. Almusallam","doi":"10.1109/WETICE49692.2020.00016","DOIUrl":"https://doi.org/10.1109/WETICE49692.2020.00016","url":null,"abstract":"Feature selection has been widely used as a pre-processing step that helps to optimise the performance of data-driven intrusion/anomaly detection systems in achieving their tasks. For example, when grouping the data into normal and outlier groups, the existence of redundant and non-representative features would reduce the accuracy of classifying the data points and would also increase the processing time. Therefore, feature selection is applied as a pre-processing step for anomaly detection systems in order to optimize their classification accuracy and running time. Most of the existing feature selection methods have limitations when dealing with high-dimensional data, as they search different subsets of features to find accurate representations of all features. Obviously, searching for different combinations of features is computationally very expensive, which makes existing work not efficient for high-dimensional data. The work carried out here, which relates to the design of a similaritybased unsupervised feature selection method for an efficient and accurate anomaly detection (UFSAD), tackles mainly the selection of reduced set of representative features from high-dimensional data without the data class labels. The selected features should improve the accuracy and performance of anomaly detection systems due to the elimination of redundant and non-representative features. The proposed UFSAD method extends the k-mean clustering algorithm to partition the features into k clusters based on a similarity measure (e.g. PCC - Pearson Correlation Coefficient, LSRE - Least Square Regression Error or MICI - Maximal Information Compression Index) in order to accurately partition the features. Then the proposed centroid-based feature selection method is used, where the feature with the closest similarity to its cluster centroid is selected as the representative feature while others are discarded. Extensive experimental work has shown that UFSAD can generate a reduced representative and non-redundant feature set that achieves good classification accuracy in comparison with well-known unsupervised features selection methods.","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129034838","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}

{"title":"Cloud interoperability based on a generic cloud service description: Mapping OWL-S to GCSD","authors":"Nour El Houda Bouzerzour, Souad Ghazouani, Y. Slimani","doi":"10.1109/WETICE49692.2020.00022","DOIUrl":"https://doi.org/10.1109/WETICE49692.2020.00022","url":null,"abstract":"The lack of standardized service descriptions in cloud environments causes the vendor lock-in, which hinders cloud service interoperability. Approaches like standards, brokers, and semantic technologies were proposed in the literature to enable cloud service interoperability. However, these propositions are specific to a certain technology or a particular cloud model, or they consider a specific cloud actor, which makes them restricted and not generic. In this paper, we discuss the importance of Cloud Service Description (CSD) standardization to enable cloud service interoperability. We propose a novel service interoperability model based on a generic CSD model (GCSD). The proposed transformation mediator uses mapping rules to transform heterogeneous cloud service descriptions into the GCSD uniform description. Consequently, it makes them interoperable. This work is in its early stages. Therefore, it covers only the transformation from OWL-S to the GCSD. Eventually, we provide use cases to illustrate the transformations from OWL-S to the GCSD.","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131228322","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}

{"title":"Web2Touch 2020–21 : Semantic Technologies for Smart Information Sharing and Web Collaboration","authors":"R. Bonacin, M. Fugini, R. Martoglia, O. Nabuco, Fatiha Saïs","doi":"10.1109/WETICE49692.2020.00053","DOIUrl":"https://doi.org/10.1109/WETICE49692.2020.00053","url":null,"abstract":"This foreword introduces a summary of themes and papers of the Web2Touch (W2T) 2020–21 Track at the 29th IEEE WETICE Conference held as a virtual Conference, in October 2020. W2T 2020–21 includes six full papers and four short papers. They all address relevant issues in the field of information sharing for collaboration, including, big data analytics, knowledge engineering, linked open data, applications of smart Web technologies, and smart care. The papers address a portfolio of hot issues in research and applications of semantics, smart technologies (e.g., IoT, sensors, devices for tele-monitoring, and smart contents management) with crucial topics, such as big data analysis, knowledge representation, smart enterprise management, among the others. This track shows how cooperative technologies based on knowledge representation, intelligent tools, and enhanced Web engineering can enhance collaborative work through smart service design and delivery, so it contributes to radically change the role of the semantic Web and applications.","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123564323","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}

{"title":"Application and preliminary evaluation of Anontool applied in the anomaly detection module","authors":"P. Bienias, A. Warzyński, G. Kolaczek","doi":"10.1109/WETICE49692.2020.00031","DOIUrl":"https://doi.org/10.1109/WETICE49692.2020.00031","url":null,"abstract":"The goal of the work is to present a preliminary result of research about trade-off between privacy and utility in network traces. The study is considered in context of anomaly detection module with using neural network. The paper presents work in progress and the preliminary results of the evaluation of a anonymization algorithms of anomaly detection module. The anonymization algorithm has been briefly described and the results of the implemented neural network detection method using anonymized flow has been discussed.","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115190600","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}

{"title":"PEA: Predicting Expert Agents approach","authors":"Afef Selmi, Zaki Brahmi, M. Gammoudi","doi":"10.1109/WETICE49692.2020.00012","DOIUrl":"https://doi.org/10.1109/WETICE49692.2020.00012","url":null,"abstract":"In multi-agent recommender system, the knowledge degree of an agent and its trust degree are two main criteria in the decision-making phase. These criteria are used to recommend the expert agent. Therefore, how to model agent and evaluate its trust is becoming a challenging issue. This problem can affect the whole prediction of expert agents. In this paper, we propose a Predicting Expert Agents approach (PEA). We applied a clustering method, Fuzzy Formal Concepts Analysis, to model agent and evaluate its trust and a probabilistic method, Theory of Belief Functions, to predict the expert agent.","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126791345","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}