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DC Field | Value | Language |
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dc.contributor.author | JAIN, SUCHITA | - |
dc.date.accessioned | 2019-09-04T06:29:26Z | - |
dc.date.available | 2019-09-04T06:29:26Z | - |
dc.date.issued | 2018-06 | - |
dc.identifier.uri | http://dspace.dtu.ac.in:8080/jspui/handle/repository/16392 | - |
dc.description.abstract | There was a time when payment was made in the forms of cash, barters, checks and other means. With the advent and rise of Internet, people have shifted from conventional forms of payment to online and digital forms of payment. It has become rather convenient to make payments online so as to avoid tensions to keep money always in pockets. As more and more payments and digital transactions have appeared online, it has attracted unwanted audience or so called scammers, hackers and threats to eavesdrop on this. Be it MITM attack or skimming or any other form of hacking attempt, these persons keep on developing newer methods for hacking. Fraud detections and risk prevention has become the foremost step to prevent frauds, robbery etc. Machine learning approaches have been put forth in the research and literature to develop models to stop and cease all this. Locations based fraud detection is one of those methods so as to identify any vulnerable attempt to steal money and rob the accounts. HMM technique will be used for this purpose as it has advantage of detecting the frauds before the actual transaction is processed with low false alerts. Distance is used as a perspective to detect the frauds. This perspective is helpful in detecting those frauds which were not detected when amount was used as an aspect. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartofseries | TD-4286; | - |
dc.subject | FRAUD DETECTION | en_US |
dc.subject | HMM TECHNIQUE | en_US |
dc.subject | E-COMMERCE TRANSACTIONS | en_US |
dc.title | LOCATION BASED FRAUD DETECTION IN E-COMMERCE TRANSACTIONS | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | M.E./M.Tech. Computer Engineering |
Files in This Item:
File | Description | Size | Format | |
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thesiscombine.pdf | 935.71 kB | Adobe PDF | View/Open |
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