Filters








1,291 Hits in 5.6 sec

Detecting Fraudulent Personalities in Networks of Online Auctioneers [chapter]

Duen Horng Chau, Shashank Pandit, Christos Faloutsos
2006 Lecture Notes in Computer Science  
In this paper we propose a novel method, 2-Level Fraud Spotting (2LFS), to model the techniques that fraudsters typically use to carry out fraudulent activities, and to detect fraudsters preemptively.  ...  Our algorithm scales linearly with the number of graph edges. Moreover, we illustrate the effectiveness of our algorithm on a real dataset collected from a large online auction site.  ...  fraud schemes, in addition to Fraud-Accomplice bipartite cores.  ... 
doi:10.1007/11871637_14 fatcat:z72w7hfu5rfcbe5reopvewvufa

E-Auction Frauds - A survey

V MNoufidali, Jobin S Thomas, Felix Arokya Jose
2013 International Journal of Computer Applications  
One major proliferation was the overture of online auctions enabling the customer community to bid for and purchase large variety of goods.  ...  The nature of Internet auctions is high degree of anonymity, number of legal opportunities to buy and sell, and low costs for entry and exit, etc..., fraudsters can easily establish frauds in auction activities  ...  To recognize online auction fraud, it is suitable to first classify the various types of online auction fraud according to the two time periods in which the fraudulent behaviour can take place, it is offline  ... 
doi:10.5120/10000-4863 fatcat:jz72cfgyrfeyrouy4wyoui5dre

P2P Lending Fraud Detection: A Big Data Approach [chapter]

Jennifer J. Xu, Yong Lu, Michael Chau
2015 Lecture Notes in Computer Science  
in China.  ...  With the help of large volumes of data from a variety of sources, we will be able to find ways to leverage rich datasets about user behaviors and transaction histories to detect loan request fraud more  ...  The authors would like to thank PPDai.com for their support for this project, and Reno Ha and Leo Lu of the University of Hong Kong for their assistance.  ... 
doi:10.1007/978-3-319-18455-5_5 fatcat:lf3c6kvkgrg7rlostibo2uswny

Netprobe

Shashank Pandit, Duen Horng Chau, Samuel Wang, Christos Faloutsos
2007 Proceedings of the 16th international conference on World Wide Web - WWW '07  
Given a large online network of online auction users and their histories of transactions, how can we spot anomalies and auction fraud?  ...  NetProbe models auction users and transactions as a Markov Random Field tuned to detect the suspicious patterns that fraudsters create, and employs a Belief Propagation mechanism to detect likely fraudsters  ...  in large scale online auction networks.  ... 
doi:10.1145/1242572.1242600 dblp:conf/www/PanditCWF07 fatcat:o2dxptvcd5dkfauehzpsi5lrde

Real Time Credit Card Fraud Detection using Computational Intelligence

Jon T. S. Quah, M. Sriganesh
2007 Neural Networks (IJCNN), International Joint Conference on  
Online banking and e-commerce have been experiencing rapid growth over the past few years and show tremendous promise of growth even in the future.  ...  It makes use of self-organization map to decipher, filter and analyze customer behavior for detection of fraud.  ...  Neural networks in fraud detection -literature review Neural Networks have been extensively put to use in the areas of banking, finance and insurance.  ... 
doi:10.1109/ijcnn.2007.4371071 dblp:conf/ijcnn/QuahS07 fatcat:oaodn4urgjf4fn2jugj6kbt3yi

Combating online in-auction fraud: Clues, techniques and challenges

Fei Dong, Sol M. Shatz, Haiping Xu
2009 Computer Science Review  
Since the in-auction fraud strategies are subtle and complex, it makes the fraudulent behavior more difficult to discover.  ...  According to one source [2], at least 31% of Americans who have Internet access regularly participate in online auctions, accounting for a sizeable total of 35 million people.  ...  Bailey for his editorial help, which significantly improved the presentation of this material.  ... 
doi:10.1016/j.cosrev.2009.09.001 fatcat:2a6krtc4h5f6rpiz642wyo4qum

Real-time credit card fraud detection using computational intelligence

Jon T.S. Quah, M. Sriganesh
2008 Expert systems with applications  
Online banking and e-commerce have been experiencing rapid growth over the past few years and show tremendous promise of growth even in the future.  ...  It makes use of self-organization map to decipher, filter and analyze customer behavior for detection of fraud.  ...  Neural networks in fraud detection -literature review Neural Networks have been extensively put to use in the areas of banking, finance and insurance.  ... 
doi:10.1016/j.eswa.2007.08.093 fatcat:5hxd5cpgqfdwdosejea5k7lcuu

A fraud detection tool in E-auctions
English

D Kavu Tatenda, Rugube Talent, Kawondera Francis, Chifamba Nyika
2016 African Journal of Mathematics and Computer Science Research  
Due to rapid growth of the use of online auctions, fraudsters have taken advantage of these platforms to participate in their own auctions in order to raise prices (a practice called shilling).  ...  This has resulted in the need to design and implement a shill detection algorithm. To eliminate this shilling problem, we designed a shilling detection algorithm integrated with an online auction.  ...  ., 2009 ) applied data mining and trust propagation techniques to detect fraudulent users in online auction systems. Generally, these techniques suffer from two drawbacks.  ... 
doi:10.5897/ajmcsr2015.0593 fatcat:xvmapkbqkzcf5ikxxf3r7womx4

Online Auction Fraud Detection in Privacy-Aware Reputation Systems

Jun-Lin Lin, Laksamee Khomnotai
2017 Entropy  
of the buyer-anonymized activities in the dataset is large.  ...  Because many auction websites have adopted privacy-aware reputation systems, the two proposed attributes should be incorporated into their fraudster detection schemes to combat these fraudulent activities  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e19070338 fatcat:bwv3cmuhgvgqbmk4tyxluesdtm

Understanding fraudulent activities in online ad exchanges

Brett Stone-Gross, Ryan Stevens, Apostolis Zarras, Richard Kemmerer, Chris Kruegel, Giovanni Vigna
2011 Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference - IMC '11  
In this paper, we present a detailed view of how one of the largest ad exchanges operates and the associated security issues from the vantage point of a member ad network.  ...  Ads can be customized based on a user's browsing behavior, geographic location, and personal interests.  ...  To avoid detection? Section 4.1: 7% of Network X's traffic is fraudulent due to a single cookie?! This seems extremely negligent on part of the ad. network.  ... 
doi:10.1145/2068816.2068843 dblp:conf/imc/Stone-GrossSZKKV11 fatcat:ekmow2r6xvcdvaovmvtkyddqb4

Ads and Fraud: A Comprehensive Survey of Fraud in Online Advertising

Shadi Sadeghpour, Natalija Vlajic
2021 Journal of Cybersecurity and Privacy  
The goal of this study is to provide a consolidated view of different categories of threats in the online advertising ecosystems.  ...  Finally, we provide a comprehensive overview of methods and techniques for the detection and prevention of fraudulent practices within those system—both from the scientific as well as the industry perspective  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jcp1040039 fatcat:hdcfm7gimvfbnk7kp2jzx2xyfi

Implementation of Fraudulent Sellers Detection System of Online Marketplaces using Machine Learning Techniques

Pooja Tyagi, M.Tech, Department of Computer Science and Engineering, Dr APJ Abdul Kalam Technical University, Lucknow (U.P.), India., Anurag Sharma, Head, Department of Computer Science and Engineering, Dr APJ Abdul Kalam Technical University, Lucknow (U.P.), India.
2021 International journal of recent technology and engineering  
To analyze the exact problem of building an interactive models for the identification of auction fraud in the entry of data into ecommerce.  ...  Where viral customers purchase products from online trading, customers may worry about fraudulent actions to get unlawful benefits from honest parties.  ...  This article aims to create a detection framework using machine learning methods of such fraudulent vendors.  ... 
doi:10.35940/ijrte.b6298.0710221 fatcat:puqcvudjprbpfcm7n5fpwsg52a

A Survey of Methodaology of Fraud Detection Using Data Mining

K Leena Kurien, Dr. Ajeet Chikkamannur
2017 International Journal of Trend in Scientific Research and Development  
Social media also offers a number of features that criminals may find attractive.  ...  Fraudsters can use social media in their efforts to appear legitimate, to hide behind anonymity, and to reach many people at low cost.  ...  The paper [20] discusses two step graph based semi supervised learning used in online auction .The social graph of online auction users and their transactions are analyzed using weighted degree centrality  ... 
doi:10.31142/ijtsrd2482 fatcat:fp4dgp4gqnfujjwcdki3p5c4oe

Detection of Fraud in Mobile Advertising using Machine Learning

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The solution proposed in this paper comprises of a social network analysis model – to detect and categorize fraudulent clicks and then test sample datasets.  ...  Hence, detection of click fraud in mobile advertising is important to ensure that no illegitimate sources are used to generate this revenue.  ...  [9] conducted another study, which proposed an online hybrid model for online in-auction fraud detection. This research showcased shilling as the primary reason behind online auction frauds.  ... 
doi:10.35940/ijitee.f4002.049620 fatcat:e2pgbdqolfcdfbmnijhn6mirja

Internet Fraud: Information for Teachers and Students

Gabriel Hudson Nkotagu
2011 Journal of International Students  
There exists a thriving economy online with large sums of money changing hands online.  ...  Internet fraud takes a number of forms with the responsible individuals changing tactics rapidly to avoid detection.  ...  Unrestricted web use often leads students to websites that participate in fraudulent activities thus exposing them to a risk of being scammed.  ... 
doaj:ce7e3b2a0a3e486f939055d3eaf50d72 fatcat:jsowroyehvbjxmvysor3cxpr6a
« Previous Showing results 1 — 15 out of 1,291 results