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Velmurugan, L.
- Latent Relation Analysis based Discovering Fraudulent Ranking Identification on Mobile Web Apps
Authors
1 AMBO University, ET
Source
Indian Journal of Science and Technology, Vol 8, No 34 (2015), Pagination:Abstract
Objective: The main objective of this work is finding a fraudulent behaviour of mobile apps where mobile app developers may generate fraudulent evidences for providing an top ranking for them. The primary goal of this work is to find out the fraudulent evidences present in the ranked mobile apps. And also this work aims to filter the mobile fraudulent ranking behaviour based on the semantic relation present among the evidences of mobile apps. Method: In the existing work, leading session methodology is introduced to leverage the fraudulent ranking activities. And also the three types of evidences are analysed and aggregated to detect the fraudulent behaviour. However this method cannot consider the relationship among the evidences that are analysed for malicious behaviour detection which will lead to an inefficient detection of fraudulent ranking behaviour. To overcome this problem, in this work latent semantic relationship among the evidences are analysed and based on the relationship exists among them fraudulent ranking behaviour is detected. This is done by constructing the vector of entities which can capture the degree of association present among the concept vectors. Application/Improvements: This proposed research methodology would be more helpful in the mobile app markets where the number of apps developed for the specific purpose has been increased considerably. In this situation, it is required to provide truthful and most popular mobile apps to the users to increase the reputation level. This proposed research methodology provides a way for increasing the reputation level of the mobile owners by detecting and eliminating the fraudulent mobile apps.Keywords
Mobile Apps, fraudulent behaviour, ranking evidences, Sematic RelationFull Text
- An Improvement of Finding Fraud Ranking of Mobile Apps Using Proportional Reversed Hazard Model
Authors
1 Department of Computer Science, AMBO University, ET
Source
Indian Journal of Innovations and Developments, Vol 5, No 10 (2016), Pagination: 1-10Abstract
Objective: To predict the fraudulent ranking behaviour for mobile apps in which the fraudulent evidences are generated by thew mobile app developers for providing the top ranking for them.
Methods: In mobile app development, the greatest challenge is ranking the mobile app by fraudulent behaviour. The fraudulent behaviour is performed because of the degradation of significant level of the mobile apps. In previous work, to leverage the fraudulent ranking behaviours, Leading Session Methodology based Evidence Aggregation (LSMEA) and Concept Vector based Review Evidence Analysis (CVREA) are developed. These methods consist of ranking based evidences, rating based evidences and review based evidences. Then, these evidence results are aggregated to detect the fraudulent ranking behaviour of mobile apps. In which, rating based evidence is described based on the user rating for corresponding mobile app. User rating is the most significant features for advertising the mobile apps. In previous analysis, user rating is analyzed by using a Gaussian distribution for computing p-value of the statistical hypotheses which is used to define the probability of user rating based on leading sessions.
Findings: The rating based evidences analysis based on the Gaussian distribution suffers from some important limitations. For large dimensions, the total number of parameters is increased quadratically and the manipulation and inversion process of large matrices may become prohibitive. In addition, Gaussian distribution is intrinsically uni-modal. Therefore, a better approximation is not provided for multimodal distributions. Such limitations are removed by introducing Proportional Reversed Hazard Model (PRHM). In this paper, an improvement of finding fraud ranking of mobile apps (IFFR) is proposed by using PRHM and the three evidence outcomes are combined for detecting the fraudulent ranking behaviour for mobile apps.
Applications/Improvements: Mostly, the proposed approach is useful for mobile app markets for developing more number of apps for the specific purpose. Therefore, the accuracy and reputation level are required for further improvement. Thus, the reputation level and accuracy is improved and the fraudulent ranking behaviour of mobile apps is removed by the proposed approach.
Keywords
Mobile Apps, Fraudulent Ranking Behaviour, Evidence Analysis, Rating Evidences, Proportional Reversed Hazard Model.Full Text
References
- H. Zhu, C. Liu, Y. Ge, H. Xiong, E. Chen. Popularity modeling for mobile apps: A sequential approach. IEEE transactions on cybernetics. 2015; 45(7), 1303-1314.
- V. Pingale, L. Kuhile, P. Phapale, P. Sapkal, S. Jaiswal. Fraud detection & prevention of mobile apps using optimal aggregation method. International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE). 2016; 6(3), 491-497.
- M. Zende, A. Gupta. Survey on fraud ranking in mobile apps. International Journal of Science and Research (IJSR). 2016; 5(2), 27-30.
- M.P.U. Gayke, S.B. Thakare. Ranking fraud detection for mobile apps using evidence aggregation method. International Journal of Engineering Development and Research (IJEDR). 2016; 4(3), 58-64.
- T.B. Gade, N.G. Pardeshi. A survey on ranking fraud detection using opinion mining for mobile apps. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE). 2015; 4(12), 337-339.
- J. Yesudoss, T. Banusankari. An efficient word alignment model for co-extracting opinion targets and opinion words from online reviews. Indian Journal of Innovations and Developments. 2015; 4(7), 1-5.
- H. Zhu, H. Xiong, Y. Ge, E. Chen. Ranking fraud detection for mobile apps: a holistic view. In: Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2013; 619-628.
- E.P. Lim, V.A. Nguyen, N. Jindal, B. Liu, H.W. Lauw. Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM international conference on Information and knowledge management. 2010; 939-948.
- W. Chai, B.K. Hoogs, B.T. Verschueren. Fuzzy ranking of financial statements for fraud detection. In: 2006 IEEE International Conference on Fuzzy Systems. 2006; 152-158.
- M. Taneja, K. Garg, A. Purwar, S. Sharma. Prediction of click frauds in mobile advertising. In: Contemporary computing (IC3), 2015 Eighth International Conference on IEEE. 2015; 162-166.
- Z. Wu, J. Wu, J. Cao, D. Tao. HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining ACM. 2012; 985-993.
- L. VelMurugan. Latent relation analysis based discovering fraudulent ranking identification on mobile web apps. Indian Journal of Science and Technology. 2015; 8(34), 1-8.
- Designing Factors of Distributed Database System: A Review
Authors
1 Department of Computer Science, Institute of Technology, Ambo University, ET
Source
Data Mining and Knowledge Engineering, Vol 12, No 1 (2020), Pagination: 7-10Abstract
A distributed database is a database in which not all storage devices are attached to a common processor.[1] The storage devices are stored in multiple computers, situated in the identical physical locality or may be isolated over a network of interconnected computers. Contrasting parallel systems, in which the processors are tightly coupled and constitute a single database system, a distributed database system consists of loosely coupled locations that share no physical mechanisms. This paper addresses various challenges in designing a distributed database. This paper also addresses designing factors in distributed database like replication, duplication, architecture, fragmentation, security and database integrity across multiple database and review the previous work done recently.
