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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
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23467
Article Published : 9055
Total Authors : 24028
Total Reviewer : 831
Total Countries : 162
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| Paper Title: | An AI-Driven Approach for Identifying Fraudulent Profiles on Social Platforms |
| Authors Name: | Minakshi , DR.RAJENDRA SINGH |
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| Author Reg. ID: |
IJRTI_210715
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| Published Paper Id: | IJRTI2603155 |
| Published In: | Volume 11 Issue 3, March-2026 |
| DOI: | |
| Abstract: | The rapid expansion of social media platforms has revolutionized digital communication and information sharing. However, the increasing number of fake and fraudulent profiles on these platforms has created significant security and privacy concerns. Fraudulent accounts are often used for malicious activities such as phishing, identity theft, spam dissemination, and misinformation campaigns. Traditional manual detection mechanisms are insufficient to manage the massive scale of social network data. This research proposes an Artificial Intelligence (AI) driven framework for identifying fraudulent profiles on social platforms. The proposed model analyzes multiple user attributes including profile information, behavioral patterns, and social network interactions using machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and Artificial Neural Networks (ANN). Experimental results demonstrate that AI-based detection systems significantly improve accuracy and efficiency in identifying fraudulent accounts compared with conventional rule-based methods. The proposed approach enhances the security and reliability of social networking environments by enabling automatic detection of malicious profiles. |
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| Cite Article: | "An AI-Driven Approach for Identifying Fraudulent Profiles on Social Platforms", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b496-b500, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603155.pdf |
| Downloads: | 00082 |
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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: IJRTI2603155
Registration ID:210715
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier):
Page No: b496-b500 Country: NEEMRANA , RAJASTHAN, INDIA Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603155 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603155 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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