Hybrid approach for detection of malicious profiles in twitter
Abstract
Emergence of social media invokes social actors to share their information digitally. Moreover, it enables them to maintain their links with other people worldwide. Due to its massive popularity, it has become a fascinating test bed to initiate various attacks. Attackers form Sybil nodes to disseminate malicious content in order to infect legitimate users aiming to steal sensitive information, such as user's credentials. Therefore, the focus of this paper is to detect malicious profiles, specifically on the Twitter microblogging platform. We propose a hybrid approach which leverages the capabilities of machine learning techniques to identify malicious profiles. Initially, Petri net structure analyzes the user's profile and various features, and these features are then used to train classifier. This is achieved to identify malicious profiles from legitimate users. Finally, to prove the efficiency, a comparative analysis of our approach is conducted with existing state-of-the-art techniques. The experimental results reveal that our approach achieves a high detection rate of 99.16% which is better than other techniques.
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Social media data analysis is central to proper detection and response to growing fake accounts.
“In general, social media data analysis stands at the heart of proper detection and response to growing fake accounts across the systems21.”
From Instagram fake profile detection using an ensemble learning method (2025) · cites this paper as [21]
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Instagram fake profile detection using an ensemble learning method
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- Authors
- Sahoo, S. R., & Gupta, B. B.
- Affiliations
- National Institute of Technology Kurukshetra (IN)
- Corresponding author
- Gupta, B.B.
- Venue
- Computers & Electrical Engineering
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 76
- Pages
- 65-81
- ISSN
- 0045-7906 (ISSN-L)
- OpenAlex
- W2924035390
- Cited by
- 107
- References
- 26
- Citations by year
- 2026: 4 · 2025: 17 · 2024: 20 · 2023: 20 · 2022: 11 · 2021: 16 · 2020: 18 · 2019: 1
- FWCI
- 14.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Electrical Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- Sybil attack, Twitter, social media security, Petri nets, microblogging, user profile features, malicious user identification
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