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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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Published Paper Details
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| Paper Title: | Content Based SMS Fraud Detection Using Supervised Learning Approach |
| Authors Name: | Mrinalini K , Chinthan Kambar , Varun V , Dr. Chandrasekar V |
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| Author Reg. ID: |
IJRTI_186826
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| Published Paper Id: | IJRTI2305204 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | Although though modern mobile phones are becoming more and more equipped with a variety of communication media messenger applications, SMS (Short Message Service) is still the preferred choice as a communication medium. It is a messaging technique that makes use of cell phones and smartphones. Spam emails and SMS have been seen to dramatically grow in volume in recent years. With the increased use of mobile devices, there has been a surge in SMS fraud, there is a type of cybercrime which uses text messages to trick and defraud people. Spam communications have been overflowing, leading to violation of personal rights, financial and may be societal issue. Like email spam, the problem of SMS spam can be addressed through governmental, commercial, or technological means. Because SMS broadcasting rates are reasonable, there is a surge in SMS spam, which some people utilize as a substitute for advertising and fraud. Hence, it becomes a major societal matter which needs to be addressed. This project's study suggests using machine learning to identify fraudulent SMS texts. This study's objective is to evaluate the Naive Bayes method with Counter Vectorization's performance in identifying and removing SMS spam. We created a dataset of SMS messages, which comprised both valid messages and fraudulent messages, and utilized this dataset to train machine learning models, by supplying useful message metadata, SMS headers can also be employed in machine learning techniques to weed out spam messages. In this report provides the design document for the proposed fraud detection system using ML approach. |
| Keywords: | SMS Fraud; Spam; Societal, Fraud Detection |
| Cite Article: | "Content Based SMS Fraud Detection Using Supervised Learning Approach", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.2144 - 2153, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305204.pdf |
| Downloads: | 000205413 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2305204
Registration ID:186826
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier):
Page No: 2144 - 2153 Country: Dakshin Kannada, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2305204 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2305204 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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