Knowledge and Data Engineering IEEE 2015 Projects
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The document discusses multiple research projects from 2015 pertaining to malware propagation in large-scale networks, ranking fraud detection in mobile apps, and effective bug triage using data reduction techniques. Each project presents an abstract that outlines the motivation, methodology, and findings, such as establishing a two-layer epidemic model for malware, detecting ranking fraud based on statistical analysis of app behavior, and improving bug triage accuracy through data processing techniques. Links to the respective projects and their languages, primarily C# and Java, are provided.
Knowledge and Data Engineering IEEE 2015 Projects
- 1. Knowledge and Data Engineering IEEE 2015 Projects Web : www.kasanpro.com Email : [email protected] List Link : http://kasanpro.com/projects-list/knowledge-and-data-engineering-ieee-2015-projects Title :Malware Propagation in Large-Scale Networks Language : C# Project Link : http://kasanpro.com/p/c-sharp/malware-propagation-large-scale-networks Abstract : Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date. In this paper, we investigate how malware propagate in networks from a global perspective. We formulate the problem, and establish a rigorous two layer epidemic model for malware propagation from network to network. Based on the proposed model, our analysis indicates that the distribution of a given malware follows exponential distribution, power law distribution with a short exponential tail, and power law distribution at its early, late and final stages, respectively. Extensive experiments have been performed through two real-world global scale malware data sets, and the results confirm our theoretical findings. Title :Discovery of Ranking Fraud for Mobile Apps Language : C# Project Link : http://kasanpro.com/p/c-sharp/ranking-fraud-discovery-mobile-apps Abstract : Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of global anomaly of App rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps' ranking, rating and review behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities. Title :Towards Effective Bug Triage with Software Data Reduction Techniques Language : Java Project Link : http://kasanpro.com/p/java/bug-triage-software-data-reduction-techniques Abstract : Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problemof data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data.We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance. Title :Towards Effective Bug Triage with Software Data Reduction Techniques Language : C#
- 2. Project Link : http://kasanpro.com/p/c-sharp/effective-bug-triage-software-data-reduction-techniques Abstract : Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problemof data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data.We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance.