Abstract
This paper outlines work on the detection of anomalous behaviour in Online Social Networks (OSNs). We present various automated techniques for identifying a ‘prodigious’ segment within a tweet, and consider tweets which are unusual because of writing style, posting sequence, or engagement level. We evaluate the mechanism by running extensive experiments over large artificially constructed tweets corpus, crawled to include randomly interpolated and abnormal Tweets. In order to successfully identify anomalies in a tweet, we aggregate more than 21 features to characterize users’ behavioural pattern. Using these features with each of our methods, we examine the effect of the total number of tweets on our ability to detect an anomaly, allowing segments of size 50 tweets 100 tweets and 200 tweets. We show indispensable improvements over a baseline in all circumstances for each method, and identify the method variant which performs persistently better than others.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andra, Z.: 10 alarming cyber security facts that threaten your data. Heimdalsecurity (2015)
Bin Tareaf, R., Berger, P., Hennig, P., Meinel, C.: Identifying audience attributes: predicting age, gender and personality for enhanced article writing. In: International Conference on Cloud and Big Data Computing, pp. 79–88. ACM (2017)
Brocardo, M.L., Traore, I., Saad, S., Woungang, I.: Authorship verification for short messages using stylometry. In: 2013 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1–6. IEEE (2013)
Corney, M., De Vel, O., Anderson, A., Mohay, G.: Gender-preferential text mining of e-mail discourse. In: 2002 Proceedings of the 18th Annual Computer Security Applications Conference, pp. 282–289. IEEE (2002)
Boutyline, A., Willer, R.: The social structure of political echo chambers: variation in ideological homophily in online networks. J. Polit. Psychol. 38, 551–569 (2017). Wiley Online Library
De Vel, O., Anderson, A., Corney, M., Mohay, G.: Mining e-mail content for author identification forensics. ACM SIGMOD Rec. 30(4), 55–64 (2001)
Bin Tareaf, R.: Tweets dataset - top 20 most followed users in Twitter social platform. In: Harvard Dataverse, V2 (2017). https://doi.org/10.7910/DVN/JBXKFD
Egele, M., Stringhini, G., Kruegel, C., Vigna, G.: COMPA: detecting compromised accounts on social networks. In: NDSS (2013)
Guthrie, D., Guthrie, L., Allison, B., Wilks, Y.: Unsupervised anomaly detection. In: IJCAI, pp. 1624–1628 (2007)
Guthrie, D., Guthrie, L., Wilks, Y.: An unsupervised approach for the detection of outliers in corpora. LREC (2008)
Koppel, M., Argamon, S., Shimoni, A.R.: Automatically categorizing written texts by author gender. Literary Linguist. Comput. 17(4), 401–412 (2002)
Schwartz, R., Tsur, O., Rappoport, A., Koppel, M.: Authorship attribution of micro-messages. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1880–1891 (2013)
Stringhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks. In: Proceedings of the 26th Annual Computer Security Applications Conference, pp. 1–9. ACM (2010)
Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: writing-style features and classification techniques. J. Assoc. Inf. Sci. Technol. 57(3), 378–393 (2006)
Acknowledgement
We would also like to show our gratitude to our master students (Henriette Dinger, Dominic Sauer, Soeren Oldag and Sebastian Kliem - Hasso Plattner Institute) who provided insight and expertise that greatly assisted the research during our research seminar.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 IFIP International Federation for Information Processing
About this paper
Cite this paper
Bin Tareaf, R., Berger, P., Hennig, P., Meinel, C. (2018). Malicious Behaviour Identification in Online Social Networks. In: Bonomi, S., Rivière, E. (eds) Distributed Applications and Interoperable Systems. DAIS 2018. Lecture Notes in Computer Science(), vol 10853. Springer, Cham. https://doi.org/10.1007/978-3-319-93767-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-93767-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93766-3
Online ISBN: 978-3-319-93767-0
eBook Packages: Computer ScienceComputer Science (R0)
