E3S Web Conf.
Volume 309, 20213rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|Number of page(s)||6|
|Published online||07 October 2021|
Detection of Fake Profiles on Twitter Using Hybrid SVM Algorithm
1 Associate Professor, Department of CSE, Teegala Krishna Reddy Engineering College, Hyderabad, Telangana.
2 Associate Professor, Department of CSE, CMR Institute of Technology, Hyderabad, Telangana.
3 Associate Professor, Department of IT, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana.
4 Professor CSE Department, Institute of Aeronautical Engineering, Hyderabad. Telangana
5 Professor CSE Department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad.
* Corresponding author: email@example.com
Establishing and management of social relationships among huge amount of users has been provided by the emerging communication medium called online social networks (OSNs). The attackers have attracted because of the rapid increasing of OSNs and the large amount of its subscriber’s personal data. Then they pretend to spread malicious activities, share false news and even stolen personal data. Twitter is one of the biggest networking platforms of micro blogging social networks in which daily more than half a billion tweets are posted most of that are malware activities. Analyze, who are encouraging threats in social networks is need to classify the social networks profiles of the users. Traditionally, there are different classification methods for detecting the fake profiles on the social networks that needed to improve their accuracy rate of classification. Thus machine learning algorithms are focused in this paper. Therefore detection of fake profiles on twitter using hybrid Support Vector Machine (SVM) algorithm is proposed in this paper. The machine learning based hybrid SVM algorithm is used in this for classification of fake and genuine profiles of Twitter accounts and applied the dimension reduction techniques, feature selection and bots. Less number of features is used in the proposed hybrid SVM algorithm and 98% of the accounts are correctly classified with proposed algorithm.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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