Knowledge and Data Engineering IEEE 2015 Projects

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Title :Malware Propagation in Large-Scale NetworksLanguage : C#Project Link : http://kasanpro.com/p/c-sharp/malware-propagation-large-scale-networksAbstract : Malware is pervasive in networks, and poses a critical threat to network security. However, we have verylimited understanding of malware behavior in networks to date. In this paper, we investigate how malware propagatein networks from a global perspective. We formulate the problem, and establish a rigorous two layer epidemic modelfor malware propagation from network to network. Based on the proposed model, our analysis indicates that thedistribution of a given malware follows exponential distribution, power law distribution with a short exponential tail, andpower law distribution at its early, late and final stages, respectively. Extensive experiments have been performedthrough two real-world global scale malware data sets, and the results confirm our theoretical findings.

Title :Discovery of Ranking Fraud for Mobile AppsLanguage : C#Project Link : http://kasanpro.com/p/c-sharp/ranking-fraud-discovery-mobile-appsAbstract : Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose ofbumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to useshady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While theimportance of preventing ranking fraud has been widely recognized, there is limited understanding and research inthis area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detectionsystem for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the activeperiods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the localanomaly 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 andreview behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregationmethod to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-worldApp data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness ofthe proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraudactivities.

Title :Towards Effective Bug Triage with Software Data Reduction TechniquesLanguage : JavaProject Link : http://kasanpro.com/p/java/bug-triage-software-data-reduction-techniquesAbstract : Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step offixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost inmanual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address theproblemof data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data.We combineinstance selection with feature selection to simultaneously reduce data scale on the bug dimension and the worddimension. To determine the order of applying instance selection and feature selection, we extract attributes fromhistorical bug data sets and build a predictive model for a new bug data set. We empirically investigate theperformance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse andMozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy ofbug triage. Our work provides an approach to leveraging techniques on data processing to form reduced andhigh-quality bug data in software development and maintenance.

Knowledge and Data Engineering IEEE 2015 Projects

Title :Towards Effective Bug Triage with Software Data Reduction TechniquesLanguage : C#Project Link : http://kasanpro.com/p/c-sharp/effective-bug-triage-software-data-reduction-techniquesAbstract : Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step offixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost inmanual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address theproblemof data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data.We combineinstance 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 fromhistorical bug data sets and build a predictive model for a new bug data set. We empirically investigate theperformance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse andMozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy ofbug triage. Our work provides an approach to leveraging techniques on data processing to form reduced andhigh-quality bug data in software development and maintenance.