Abstract
Twitter is a microblogging service where users can post short messages and communicate with millions of users instantaneously. Twitter has been used for marketing, political campaigns, and during catastrophic events. Unfortunately, Twitter has been exploited by spammers and cybercriminals to post spam, spread malware, and launch different kinds of cyberattacks. The ease of following another user on Twitter, the posting of shortened URLs in tweets, the use of trending hashtags in tweets, and so on, have made innocent users the victims of various cyberattacks. This chapter reviews recent methods to detect spam, spammers, cybercus content, and suspicious users on Twitter. It also presents a unified framework for modeling hreats on Twitter are discussed, specifically in the context of big data and adversarial machine learning.
Approved for Public Release; Distribution Unlimited: 88ABW-2017-1553, dated 05 Apr 2017.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
For example, http://google.com.
References
Digital in 2016. http://wearesocial.com/special-reports/digital-in-2016, Jan 2016
Twitter Usage. https://about.twitter.com/company, Feb 2017
Cisco (2013). https://www.cisco.com/web/offer/gist_ty2_asset/Cisco_2013_ASR.pdf
Twitter Death Rumor Leads to Spike in Oil Prices. http://mashable.com/2012/08/07/twitter-rumor-oil-price, Aug 2012
FBI Investigating Central Command Twitter Hack. http://www.cnbc.com/2015/01/12/us-central-command-twitter-hacked.html, Jan 2015
Iranian Hackers Attack State Dept. via Social Media Accounts. https://www.nytimes.com/2015/11/25/world/middleeast/iran-hackers-cyberespionage-state-department-social-media.html, Nov 2015
Sony Music’s Twitter Hacked, Fake Britney Spears Death Tweets Sent. http://www.reuters.com/article/us-sony-twitter-cyber-idUSKBN14F11D, Dec 2016
Thomas K, Nicol DM (2010) The Koobface botnet and the rise of social malware. In: Proceedings of the 5th International conference on malicious and unwanted software, Oct 2010, pp 63–70
Twitter Malware: Spreading More Than Just Ideas. https://securityintelligence.com/twitter-malware-spreading-more-than-just-ideas, Apr 2013
Social Media a Growing Risk for Corporate Security (2016). https://gdssummits.com/app/uploads/sites/1/2016/03/Social-media-a-growing-risk-for-corporate-security-whitepaper.pdf
Rao P, Katib A, Kamhoua C, Kwiat K, Njilla L (2016) Probabilistic inference on Twitter data to discover suspicious users and malicious content. In: Proceedings of the 2nd IEEE International symposium on security and privacy in social networks and big data (SocialSec), Nadi, Fiji, pp 1–8
Bitly (2017). https://bitly.com
Twitter Developer Documentation (2017). https://dev.twitter.com/rest/public
Lee K, Caverlee J, Webb S (2010) Uncovering social spammers: social honeypots + machine learning. In: Proceedings of the 33rd International SIGIR conference, pp 435–442
Myspace (2017). https://myspace.com
Stringhini G, Kruegel C, Vigna G (2010) Detecting spammers on social networks. In: Proceedings of the 26th annual computer security applications conference, pp 1–9
Facebook (2017). https://www.facebook.com
Grier C, Thomas K, Paxson V, Zhang M (2010) @Spam: the underground on 140 characters or less. In: Proceedings of the 17th ACM conference on computer and communications security, Chicago, Illinois, USA, pp 27–37
Wang D, Navathe SB, Liu L, Irani D, Tamersoy A, Pu C (2013) Click traffic analysis of short URL spam on Twitter. In: Proceedings of 9th International conference on collaborative computing: networking, applications and worksharing, Oct 2013, pp 250–259
Ghosh S, Viswanath B, Kooti F, Sharma NK, Korlam G, Benevenuto F, Ganguly N, Gummadi KP (2012) Understanding and combating link farming in the Twitter social network. In: Proceedings of the 21st International conference on world wide web, pp 61–70
Yang C, Harkreader R, Zhang J, Shin S, Gu G (2012) Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on Twitter. In: Proceedings of the 21st International conference on the world wide web, pp 71–80
Sanzgiri A, Hughes A, Upadhyaya S (2013) Analysis of malware propagation in Twitter. In: Proceedings of the 32nd IEEE symposium on reliable distributed systems, pp 195–204
Lee S, Kim J (2013) WarningBird: a near real-time detection system for suspicious URLs in Twitter stream. IEEE Trans Dependable Secur Comput 10(3):183–195
Burnap P, Javed A, Rana OF, Awan MS (2015) Real-time classification of malicious URLs on Twitter using machine activity data. In: Proceedings of the 2015 IEEE/ACM International conference on advances in social networks analysis and mining 2015, pp 970–977
Richardson M, Domingos P (2006) Markov logic networks. Mach Learn 62(1–2):107–136
Poon H, Domingos P (2008) Joint unsupervised coreference resolution with Markov logic. In: Proceedings of the conference on empirical methods in NLP, pp 650–659
Mccallum A, Wellner B (2004) Conditional models of identity uncertainty with application to noun coreference. In: Saul LK, Weiss Y, Bottou L (eds) Advances in neural information processing systems 17. MIT Press, Cambridge, MA, pp 905–912
Singla P, Domingos P (2006) Entity resolution with Markov logic. In: Proceedings of the 6th International conference on data mining, ICDM ’06, pp 572–582
Chakrabarti S, Dom B, Indyk P (1998) Enhanced hypertext categorization using hyperlinks. In: Proceedings of the 1998 ACM SIGMOD International conference on management of data, Seattle, Washington, USA, pp 307–318
Poon H, Domingos P (2007) Joint inference in information extraction. In: Proceedings of the 22nd national conference on artificial intelligence—volume 1, Vancouver, British Columbia, Canada, pp 913–918
Singla P, Domingos P (2005) Discriminative training of Markov logic networks. In: Proceedings of the 20th AAAI conference on artificial intelligence, pp 868–873
Jha AK, Gogate V, Meliou A, Suciu D (2010) Lifted inference seen from the other side: the tractable features. In: Proceedings of advances in neural information processing systems (NIPS), pp 973–981
Sarkhel S, Singla P, Gogate V (2015) Fast lifted MAP inference via partitioning. In: Proceedings of advances in neural information processing systems (NIPS), pp 3240–3248
Niu F, Ré C, Doan A, Shavlik J (2011) Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS. Proc VLDB Endow 4(6):373–384
Chen Y, Wang DZ (2014) Knowledge expansion over probabilistic knowledge bases. In: Proceedings of the 2014 ACM SIGMOD conference, pp 649–660
VirusTotal (2017). https://virustotal.com
Big Data: Seizing Opportunities, Preserving Values (2014). http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_5.1.14_final_print.pdf
Li M, Andersen DG, Park JW, Smola AJ, Ahmed A, Josifovski V, Long J, Shekita EJ, Su B-Y (2014) Scaling distributed machine learning with the parameter server. In: 11th OSDI conference, Oct 2014, pp 583–598
Low Y, Gonzalez J, Kyrola A, Bickson D, Guestrin C, Hellerstein JM (2012) Distributed GraphLab: a framework for machine learning in the cloud. Proc. VLDB Endow 5(8):716–727
Meng X, Bradley JK, Yavuz B, Sparks ER, Venkataraman S, Liu D, Freeman J, Tsai DB, Amde M, Owen S, Xin D, Xin R, Franklin MJ, Zadeh R, Zaharia M, Talwalkar A (2015) MLlib: machine learning in Apache Spark. CoRR. arXiv:1505.06807
Huang L, Joseph AD, Nelson B, Rubinstein BI, Tygar JD (2011) Adversarial machine learning. In: Proceedings of the 4th ACM workshop on security and artificial intelligence, Chicago, Illinois, USA, pp 43–58
Nelson B, Barreno M, Jack Chi F, Joseph AD, Rubinstein BIP, Saini U, Sutton C, Tygar JD, Xia K (2009) Misleading learners: co-opting your spam filter. Springer US, Boston, MA, pp 17–51
Acknowledgements
This work was performed while the first author held an NRC Research Associateship award at Air Force Research Lab, Rome, New York. The authors would like to thank the anonymous reviewers for their comments and suggestions, and Anas Katib for his assistance.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Rao, P., Kamhoua, C., Njilla, L., Kwiat, K. (2018). Methods to Detect Cyberthreats on Twitter. In: Karampelas, P., Bourlai, T. (eds) Surveillance in Action. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-68533-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-68533-5_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68532-8
Online ISBN: 978-3-319-68533-5
eBook Packages: Political Science and International StudiesPolitical Science and International Studies (R0)