Publication Date

9-14-2018

Document Type

Article

Department

Applied Data Science

Disciplines

Computer Sciences

Publication Title

Development Engineering

Volume

3

DOI

10.1016/j.deveng.2018.08.002

First Page

218

Last Page

233

Abstract

Presidential elections can impact world peace, global economics, and overall well-being. Recent news indicates that fraud on the Web has played a substantial role in elections, particularly in developing countries in South America and the public discourse, in general. To protect the trustworthiness of the Web, in this paper, we present a novel framework using statistical techniques to help detect veiled Web fraud attacks in Online Social Networks (OSN). Specific examples are used to demonstrate how some statistical techniques, such as the Kalman Filter and the modified CUSUM, can be applied to detect various attack scenarios. A hybrid data set, consisting of both real user tweets collected from Twitter and simulated fake tweets is constructed for testing purposes. The efficacy of the proposed framework has been verified by computing metrics, such as Precision, Recall, and Area Under the ROC curve. The algorithms achieved up to 99.9% accuracy in some scenarios and are over 80% accurate for most of the other scenarios.

Keywords

Cumulative sum, Discrete Kalman Filter, Sentiment analysis, Online Social Networks, Twitter bot, Attack injection, Microblogging

Comments

This is the Version of Record and can also be read online here.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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