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Open Access
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Abhishek Agrawal, Priyanka Baste, Snigdha Take, Sachin Tupe, S. N. Botekar
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- Student, Student, Student, Student, Professor Department Of Computer Engineering, Jawahar Education Society’s Institute Of Engineering, Nashik, Department Of Computer Engineering, Jawahar Education Society’s Institute Of Engineering, Nashik, Department Of Computer Engineering, Jawahar Education Society’s Institute Of Engineering, Nashik, Department Of Computer Engineering, Jawahar Education Society’s Institute Of Engineering, Nashik, Department Of Computer Engineering, Jawahar Education Society’s Institute Of Engineering, Nashik Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra India, India, India, India, India
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Abstract
nThe growing frequency of fake accounts on social media platforms underscores the critical necessity for effective detection methods. In response to this challenge, our study leverages state-of-the-art machine learning techniques to identify and counter deceptive entities effectively. By conducting a thorough analysis of social media data, our approach unveils intricate patterns indicative of fraudulent accounts, enabling proactive measures against them. Through the application of advanced algorithms, we present a comprehensive methodology for the identification and mitigation of such accounts, addressing current online security challenges and anticipating future trends. Moreover, our research not only contributes significantly to enhancing cybersecurity within the realm of social media but also lays the groundwork for future advancements in this field. By integrating machine learning methods, our study offers a nuanced and highly effective approach, thereby bolstering the overall security landscape of online platforms. This paper serves to underscore the considerable strides made in social media security and provides a robust foundation for further research and development initiatives aimed at safeguarding users from malicious activities.
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Keywords: [ML] Machine Learning, [NLP] Natural Language Processing, [ROC] Receiver Operating Characteristic, [AUC] Area Under the Curve, [TPR] True Positive Rate, [FPR] False Positive Rate, [SVM] Support Vector Machines.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Mobile Computing, Communications & Mobile Networks(jomccmn)]
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References
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Journal of Mobile Computing, Communications & Mobile Networks
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | March 22, 2024 | |
| Accepted | June 25, 2024 | |
| Published | June 29, 2024 |
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