Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network

Bibliographic Data

ID22107937
AuthorsRashmi Ranjan Rout (0000-0003-3457-6633, National Institute of Technology Warangal), Greeshma Lingam (0009-0003-1039-6491, National Institute of Technology Warangal), D V L N Somayajulu (National Institute of Technology Warangal)
Year2020
Volume7
Issue4
Pages1004-1018
Publication date2020-08-01
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2020.2992223
OpenAlexW3025451404
LanguageEN
Citations received7
References cited45

Malicious social bots generate fake tweets and automate their social relationships either by pretending like a follower or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweet in order to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, a learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes' theorem, and the indirect trust is derived from the Dempster-Shafer theory (DST) to determine the trustworthiness of each participant accurately. Experimentation has been performed on two Twitter data sets, and the results illustrate that the proposed algorithm achieves improvement in precision, recall, F-measure, and accuracy compared with existing approaches for MSBD

Botnet · Computer security · Server · Social graph · Social media · The Internet · Trustworthiness · World Wide Web · Computer Science · Internet Traffic Analysis and Secure E-voting · Network Security and Intrusion Detection · Spam and Phishing Detection

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