An Empirical Study of Factors Affecting the Rate of Spam
Resumo
Several factors may influence the number of spam received by email users, from user's profile information such as age or nationality to the way the email account is exposed on the Web. We propose a replication study of an experiment conducted more than a decade ago to understand the changes in the dynamics of the business of spam. To that end, using real email addresses created and managed only for the experiment, we simulate four different behavior profiles: i) interaction on social networks, ii) purchase in e-commerce sites, iii) interaction on forums and message boards and iv) use of file sharing tools, and analyze the amount of spam received on those accounts. The results indicate that linking an email account to a social network is the most significant influence on the spam rate.
Referências
Almaatouq, A., Shmueli, E., Nouh, M., Alabdulkareem, A., Singh, V. K., Alsaleh, M., Alari, A., Alfaris, A., and Pentland, A. (2016). If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts. International Journal of Information Security, (February):1–17.
Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Sakkis, G., Spyropoulos, C. D., and Stamatopoulos, P. (2000). Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach. Proceedings of the workshop Machine Learning and Textual Information Access, (September 2000):1–12.
Blanzieri, E. and Bryl, A. (2008). A survey of learning-based techniques of email spam ltering. Articial Intelligence Review, 29(1):63–92.
Cerf, V. G. (2005). Spam, spim, and spit. Communications of the ACM, 48(4):39.
Cheng, C. K., Paré, D. E., Collimore, L. M., and Joordens, S. (2011). Assessing the effectiveness of a voluntary online discussion forum on improving students’ course performance. Computers and Education, 56(1):253–261.
Clayton, R. (2008). Do Zebras get more Spam than Aardvarks? In Proceedings of the Fifth Conference on Email and Anti-Spam.
Dhinakaran, C., Lee, J. K., Nagamalai, D., and Chae, C. J. (2007). An empirical study of spam and spam vulnerable email accounts. Proceedings of Future Generation Communication and Networking, Main Conference Papers, Vol 1, 1:407–412.
Ezpeleta, E., Zurutuza, U., and Hidalgo, J. M. G. (2016). A study of the personalization of spam content using facebook public information. Logic Journal of the IGPL, 25(1):30– 41.
Garg, V. and Niliadeh, S. (2013). Craigslist scams and community composition: Investigating online fraud victimization. In Security and Privacy Workshops (SPW), 2013 IEEE, pages 123–126. IEEE.
Hann, I.-H., Hui, K.-L., Lai, Y.-L., Lee, S.-Y. T., and Png, I. P. (2006). Who gets spammed? Communications of the ACM, 49(10):83–87.
Hart, M. (2008). Do online buying behaviour and attitudes to web personalization vary by age group? In Proceedings of the 2008 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries: Riding the Wave of Technology, SAICSIT ’08, pages 86–93, New York, NY, USA. ACM.
Häubl, G. and Trifts, V. (2000). Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science, 19(1):421.
Jacobsson, A. and Carlsson, B. (2004). Privacy and Spam: Empirical Studies of UnIn Proceedings of IFIP Summer School on Risks & solicited Commercial E-Mail. Challenges of the Network Society, pages 241–251.
Jin, L., Chen, Y., Wang, T., Hui, P., and Vasilakos, A. V. (2013). Understanding user IEEE Communications Magazine, behavior in online social networks: A survey. 51(9):144–150.
Jung, J. and Sit, E. (2004). An empirical study of spam trafc and the use of DNS black lists. 4th ACM SIGCOMM conference on Internet measurement, pages 370–375.
Listandyou (2013). 10 services to create free email accounts.
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., and Bhattacharjee, B. (2007). Measurement and analysis of online social networks. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement IMC ’07, pages 29–42.
Schryen, G. (2007). The impact that placing email addresses on the Internet has on the receipt of spam: An empirical analysis. Computers and Security, 26(5):361–372.
Siponen, M. and Stucke, C. (2006). Effective anti-spam strategies in companies: An international study. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06), volume 6, pages 127c–127c.
Smith, A. and Anderson, M. (2016). Online Shopping and E-Commerce. Technical report, Pew Research Center.
Whang, L. S.-M., Lee, S., and Chang, G. (2003). Internet over-users’ psychological proles: a behavior sampling analysis on internet addiction. Cyberpsychology & behavior, 6(2):143–150.
Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Sakkis, G., Spyropoulos, C. D., and Stamatopoulos, P. (2000). Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach. Proceedings of the workshop Machine Learning and Textual Information Access, (September 2000):1–12.
Blanzieri, E. and Bryl, A. (2008). A survey of learning-based techniques of email spam ltering. Articial Intelligence Review, 29(1):63–92.
Cerf, V. G. (2005). Spam, spim, and spit. Communications of the ACM, 48(4):39.
Cheng, C. K., Paré, D. E., Collimore, L. M., and Joordens, S. (2011). Assessing the effectiveness of a voluntary online discussion forum on improving students’ course performance. Computers and Education, 56(1):253–261.
Clayton, R. (2008). Do Zebras get more Spam than Aardvarks? In Proceedings of the Fifth Conference on Email and Anti-Spam.
Dhinakaran, C., Lee, J. K., Nagamalai, D., and Chae, C. J. (2007). An empirical study of spam and spam vulnerable email accounts. Proceedings of Future Generation Communication and Networking, Main Conference Papers, Vol 1, 1:407–412.
Ezpeleta, E., Zurutuza, U., and Hidalgo, J. M. G. (2016). A study of the personalization of spam content using facebook public information. Logic Journal of the IGPL, 25(1):30– 41.
Garg, V. and Niliadeh, S. (2013). Craigslist scams and community composition: Investigating online fraud victimization. In Security and Privacy Workshops (SPW), 2013 IEEE, pages 123–126. IEEE.
Hann, I.-H., Hui, K.-L., Lai, Y.-L., Lee, S.-Y. T., and Png, I. P. (2006). Who gets spammed? Communications of the ACM, 49(10):83–87.
Hart, M. (2008). Do online buying behaviour and attitudes to web personalization vary by age group? In Proceedings of the 2008 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries: Riding the Wave of Technology, SAICSIT ’08, pages 86–93, New York, NY, USA. ACM.
Häubl, G. and Trifts, V. (2000). Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science, 19(1):421.
Jacobsson, A. and Carlsson, B. (2004). Privacy and Spam: Empirical Studies of UnIn Proceedings of IFIP Summer School on Risks & solicited Commercial E-Mail. Challenges of the Network Society, pages 241–251.
Jin, L., Chen, Y., Wang, T., Hui, P., and Vasilakos, A. V. (2013). Understanding user IEEE Communications Magazine, behavior in online social networks: A survey. 51(9):144–150.
Jung, J. and Sit, E. (2004). An empirical study of spam trafc and the use of DNS black lists. 4th ACM SIGCOMM conference on Internet measurement, pages 370–375.
Listandyou (2013). 10 services to create free email accounts.
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., and Bhattacharjee, B. (2007). Measurement and analysis of online social networks. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement IMC ’07, pages 29–42.
Schryen, G. (2007). The impact that placing email addresses on the Internet has on the receipt of spam: An empirical analysis. Computers and Security, 26(5):361–372.
Siponen, M. and Stucke, C. (2006). Effective anti-spam strategies in companies: An international study. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06), volume 6, pages 127c–127c.
Smith, A. and Anderson, M. (2016). Online Shopping and E-Commerce. Technical report, Pew Research Center.
Whang, L. S.-M., Lee, S., and Chang, G. (2003). Internet over-users’ psychological proles: a behavior sampling analysis on internet addiction. Cyberpsychology & behavior, 6(2):143–150.
Publicado
10/05/2018
Como Citar
MIANI, Rodrigo Sanches; OLIVEIRA, Danielle; PARK, Kil Jin Brandini; ZARPELÃO, Bruno Bogaz.
An Empirical Study of Factors Affecting the Rate of Spam. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 36. , 2018, Campos do Jordão.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 239-252.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2018.2419.
