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access icon free Economic feasibility study for stealthy socialbot network establishment

© The Institution of Engineering and Technology
Received 26/03/2019, Accepted 08/06/2020, Revised 16/04/2020, Published 18/06/2020

Socialbots are intelligent software that controls all behaviour of fake accounts in an online social network. Since they are armed with detection evasion techniques, it is valuable to be able to determine the effectiveness of these techniques. In this study, an analytical model is developed to estimate a lower bound for the cost of automatic establishment of a socialbot network. Moreover, by considering fake accounts purchasing as an establishment strategy, an upper bound is suggested for acceptable costs. These two boundaries are compared to decide on the economic feasibility of a socialbot network design strategy. To demonstrate the practicality and effectiveness of the model, two case studies are investigated. They show that although designing a fully stealthy socialbot network is economically feasible, the infiltration time would be unacceptable. Thus, this ideal situation in which the establishment is fully stealthy, performs in a tolerable time, and satisfactory infiltration scale, is impractical. A possible solution could be achieved by reducing the time and cost in exchange for less stealthy behaviour while the infiltration scale kept unchanged. Since the model presents a trade-off between stealthiness, time, and cost, it is a useful tool facilitating the design of a possible strategy.

Inspec keywords: purchasing; social networking (online); security of data

Other keywords: socialbot network design strategy; online social network; stealthy behaviour; analytical model; detection evasion techniques; intelligent software; economic feasibility study; infiltration time; fully stealthy socialbot network; automatic establishment; infiltration scale; acceptable costs; stealthy socialbot network establishment; establishment strategy; fake accounts

Subjects: Data security; Information networks

References

    1. 1)
      • 1. Li, H., Mukherjee, A., Liu, B., et al: ‘Detecting campaign promoters on twitter using markov random fields’. 2014 IEEE Int. Conf. on Data Mining (ICDM), Shenzhen, People's Republic of China, 2014, pp. 290–299.
    2. 2)
      • 54. Gong, N.Z., Frank, M., Mittal, P.: ‘Sybilbelief: A semi-supervised learning approach for structure-based sybil detection’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (6), pp. 976–987.
    3. 3)
      • 48. Zheng, X., Zeng, Z., Chen, Z., et al: ‘Detecting spammers on social networks’, Neurocomputing, 2015, 159, (1), pp. 27–34.
    4. 4)
      • 42. Wagner, C., Mitter, S., Körner, C., et al: ‘When social bots attack: modeling susceptibility of users in online social networks’, Making Sense of Microposts (# MSM2012), 2012, vol. 2, no. 4, pp. 1951–1959.
    5. 5)
      • 30. Ravasz, E., Barabási, A.-L.: ‘Hierarchical organization in complex networks’, Phys. Rev. E, 2003, 67, (2), pp. 026 112–1–026 112–7.
    6. 6)
      • 58. Viswanath, B., Mislove, A., Cha, M., et al: ‘On the evolution of user interaction in facebook’. Proc. of the 2nd ACM SIGCOMM Workshop on Social Networks (WOSN'09), 2009, pp. 37–42.
    7. 7)
      • 35. Mele, A.: ‘A structural model of dense network formation’, Econometrica, 2017, 85, (3), pp. 825–850.
    8. 8)
      • 55. Wang, B., Zhang, L., Gong, N.Z.: ‘Sybilscar: Sybil detection in online social networks via local rule based propagation’. IEEE INFOCOM 2017-IEEE Conf. on Computer Communications, Atlanta, GA, USA, 2017, pp. 1–9.
    9. 9)
      • 45. Shi, P., Zhang, Z., Choo, K.-K.R.: ‘Detecting malicious social bots based on clickstream sequences’, IEEE Access, 2019, 7, (1), pp. 28 855–28 862.
    10. 10)
      • 9. Faghani, M.R., Matrawy, A., Lung, C.-H.: ‘A study of trojan propagation in online social networks’. 2012 5th Int. Conf. on New Technologies, Mobility and Security (NTMS), Istanbul, Turkey, 2012, pp. 1–5.
    11. 11)
      • 17. Lee, K., Caverlee, J., Webb, S.: ‘Uncovering social spammers: social honeypots+machine learning’. Proc. of the 33rd int. ACM SIGIR Conf. on Research and development in information retrieval, Geneva, Switzerland, 2010, pp. 435–442.
    12. 12)
      • 8. Yan, G., Chen, G., Eidenbenz, S., et al: ‘Malware propagation in online social networks: nature, dynamics, and defense implications’. Proc. of the 6th ACM Symp. on Information, Computer and Communications Security, Hong Kong, 2011, pp. 196–206.
    13. 13)
      • 3. Ratkiewicz, J., Conover, M., Meiss, M., et al: ‘Truthy: mapping the spread of astroturf in microblog streams’. Proc. of the 20th int. Conf. companion on World wide web, Bangalore, India, 2011, pp. 249–252.
    14. 14)
      • 60. Hamby, D.: ‘A review of techniques for parameter sensitivity analysis of environmental models’, Environ. Monit. Assess., 1994, 32, (2), pp. 135–154.
    15. 15)
      • 43. Stringhini, G., Kruegel, C., Vigna, G.: ‘Detecting spammers on social networks’. Proc. of the 26th annual computer security applications Conf., Austin, Texas, USA, 2010, pp. 1–9.
    16. 16)
      • 4. Ratkiewicz, J., Conover, M., Meiss, M.R., et al: ‘Detecting and tracking political abuse in social media', ICWSM, 2011, 11, pp. 297–304.
    17. 17)
      • 41. Motoyama, M., Levchenko, K., Kanich, C., et al: ‘Re: captchas-understanding captcha-solving services in an economic context’. Proc. of the 19th USENIX Conf. on Security, ser. USENIX Security'10, Washington, D.C. USA, 2010, pp. 28–28.
    18. 18)
      • 46. Cao, Q., Yang, X., Yu, J., et al: ‘Uncovering large groups of active malicious accounts in online social networks’. Proc. of the 2014 ACM SIGSAC Conf. on Computer and Communications Security, Scottsdale Arizona USA, 2014, pp. 477–488.
    19. 19)
      • 34. Badev, A.: ‘Discrete games in endogenous networks: theory and policy’ (University of Pennsylvania Working Paper, 2013), pp. 1–52.
    20. 20)
      • 49. Yu, H., Kaminsky, M., Gibbons, P.B., et al: ‘Sybilguard: defending against sybil attacks via social networks’, ACM SIGCOMM Comput. Commun.Rev., 2006, 36, (4), pp. 267–278.
    21. 21)
      • 11. Bollen, J., Mao, H., Zeng, X.: ‘Twitter mood predicts the stock market’, J. Comput. Sci., 2011, 2, (1), pp. 1–8.
    22. 22)
      • 37. Badev, A.: ‘Nash equilibria on (un) stable networks’. Federal Reserve Board Working Paper, 2018, pp. 1–45, https://arxiv.org/abs/1901.00373.
    23. 23)
      • 24. Khademi, M., Moghaddam, S.H., Abbaspour, M.: ‘An empirical study of the effect of profile and behavioral characteristics on the infiltration rate of socialbots’. 2017 Iranian Conference on Electrical Engineering (ICEE), 2017, pp. 2200–2205.
    24. 24)
      • 32. Mayer, A., Puller, S.L.: ‘The old boy (and girl) network: social network formation on university campuses’, J. Public. Econ., 2008, 92, (1–2), pp. 329–347.
    25. 25)
      • 20. Elyashar, A., Fire, M., Kagan, D., et al: ‘Homing socialbots: intrusion on a specific organization's employee using socialbots’. Proc. of the 2013 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining, Niagara, Ontario, Canada, 2013, pp. 1358–1365.
    26. 26)
      • 59. ‘Buy facebook accounts with real friends buy facebook accounts with friends’, available at http://cybermarketingservice.com/buy-facebook-accounts/buy-facebookaccounts-with-real-friends/, (Accessed on 10/10/2017).
    27. 27)
      • 39. Leung, M.P.: ‘Two-step estimation of network-formation models with incomplete information’, J. Econom., 2015, 188, (1), pp. 182–195.
    28. 28)
      • 5. Bilge, L., Strufe, T., Balzarotti, D., et al: ‘All your contacts are belong to us: automated identity theft attacks on social networks’. Proc. of the 18th int. Conf. on World wide web, Madrid Spain, 2009, pp. 551–560.
    29. 29)
      • 36. Mele, A., Zhu, L.: ‘Approximate variational estimation for a model of network formation’. Available at https://arxiv.org/abs/1702.00308, 2019, pp. 1–51.
    30. 30)
      • 7. Thomas, K., McCoy, D., Grier, C., et al: ‘Trafficking fraudulent accounts: the role of the underground market in twitter spam and abuse'. USENIX Security Symp., Washington, D.C. USA, 2013, pp. 195–210.
    31. 31)
      • 44. Wang, G., Konolige, T., Wilson, C., et al: ‘You are how you click: clickstream analysis for sybil detection’. The 22nd USENIX Security Symp. (USENIX Security 13), Washington, D.C. USA, 2013, pp. 241–256.
    32. 32)
      • 52. Fortunato, S.: ‘Community detection in graphs’, Phys. Rep., 2010, 486, (3), pp. 75–174.
    33. 33)
      • 13. Bessi, A., Ferrara, E.: ‘Social bots distort the 2016us presidential election online discussion’, First Monday, 2016, 21, (11), https://doi.org/10.5210/fm.v21i11.7090.
    34. 34)
      • 51. Viswanath, B., Post, A., Gummadi, K.P., et al: ‘An analysis of social network-based sybil defenses’, ACM SIGCOMM Comput. Commun. Rev., 2010, 40, (4), pp. 363–374.
    35. 35)
      • 14. Wei, W., Xu, F., Tan, C.C., et al: ‘Sybildefender: A defense mechanism for sybil attacks in large social networks’, IEEE Trans. Parallel Distrib. Syst., 2013, 24, (12), pp. 2492–2502.
    36. 36)
      • 57. Allenby, R.B., Slomson, A.: ‘How to count: an introduction to combinatorics' (CRC Press, Boca Raton, FL, USA, 2011).
    37. 37)
      • 10. Faghani, M.R., Saidi, H.: ‘Malware propagation in online social networks’. 2009 4th Int. Conf. on Malicious and Unwanted Software (MALWARE), Montreal, QC, Canada, 2009, pp. 8–14.
    38. 38)
      • 47. Yang, Z., Wilson, C., Wang, X., et al: ‘Uncovering social network sybils in the wild’, ACM Trans. Knowl. Discov. Data (TKDD), 2014, 8, (1), pp. 1–29.
    39. 39)
      • 53. Cao, Q., Sirivianos, M., Yang, X., et al: ‘Aiding the detection of fake accounts in large scale social online services’. Proc. of the 9th USENIX Conf. on Networked Systems Design and Implementation. USENIX Association, Berkeley, CA USA, 2012, pp. 15–15.
    40. 40)
      • 2. Mourtada, R., Salem, F.: ‘Civil movements: the impact of facebook and twitter’, Arab Soc. Media Rep., 2011, 1, (2), pp. 1–30.
    41. 41)
      • 28. Albert, R., Barabási, A.-L.: ‘Statistical mechanics of complex networks’, Rev. Mod. Phys., 2002, 74, (1), p. pp. 47–97.
    42. 42)
      • 31. Jackson, M.O., Rogers, B.W.: ‘Meeting strangers and friends of friends: how random are social networks?’, Am. Econ. Rev., 2007, 97, (3), pp. 890–915.
    43. 43)
      • 19. Boshmaf, Y., Muslukhov, I., Beznosov, K., et al: ‘Design and analysis of a social botnet’, Comput. Netw., 2013, 57, (2), pp. 556–578.
    44. 44)
      • 56. Wang, B., Jia, J., Zhang, L., et al: ‘Structure-based sybil detection in social networks via local rule-based propagation’, IEEE Trans. Netw. Sci. Eng., 2018, 6, (3), pp. 523–537.
    45. 45)
      • 16. Benevenuto, F., Magno, G., Rodrigues, T., et al: ‘Detecting spammers on twitter’. Collaboration, electronic messaging, anti-abuse and spam Conf. (CEAS), Redmond, USA, 2010, pp. 75–83.
    46. 46)
      • 12. Woolley, S.C.: ‘Automating power: social bot interference in global politics’, First Monday, 2016, 21, (4), https://doi.org/10.5210/fm.v21i4.6161.
    47. 47)
      • 23. Fazil, M., Abulaish, M.: ‘Why a socialbot is effective in twitter? a statistical insight’. 2017 9th Int. Conf. on Communication Systems and Networks (COMSNETS), Bangalore, India, January 2017, pp. 564–569.
    48. 48)
      • 50. Yu, H., Gibbons, P.B., Kaminsky, M., et al: ‘Sybillimit: A near-optimal social network defense against sybil attacks’. 2008 IEEE Symp. on Security and Privacy (SP 2008), Oakland, California, USA, 2008, pp. 3–17.
    49. 49)
      • 22. Freitas, C., Benevenuto, F., Ghosh, S., et al: ‘Reverse engineering socialbot infiltration strategies in twitter’. Proc. of the 2015 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining 2015, Paris, France, 2015, pp. 25–32.
    50. 50)
      • 29. Watts, D.J., Strogatz, S.H.: ‘Collective dynamics of ‘small-world'networks’, Nature, 1998, 393, (6684), p. pp. 440–447.
    51. 51)
      • 18. Boshmaf, Y., Muslukhov, I., Beznosov, K., et al: ‘The socialbot network: when bots socialize for fame and money’. Proc. of the 27th annual computer security applications Conf., Orlando Florida USA, 2011, pp. 93–102.
    52. 52)
      • 6. Thomas, K., Grier, C., Paxson, V.: ‘Adapting social spam infrastructure for political censorship'. Proc. of the 5th USENIX Conf. on Large-Scale Exploits and Emergent Threats, San Jose, CA, USA, 2012, pp. 13–13.
    53. 53)
      • 33. Graham, B.S.: ‘Homophily and transitivity in dynamic network formation’, Nat. Bureau Econ. Res., Tech. Rep., 2016Article no. 22186.
    54. 54)
      • 27. Zheng, T., Salganik, M.J., Gelman, A.: ‘How many people do you know in prison? Using overdispersion in count data to estimate social structure in networks’, J. Am. Stat. Assoc., 2006, 101, (474), pp. 409–423.
    55. 55)
      • 15. Fire, M., Katz, G., Elovici, Y.: ‘Strangers intrusion detection-detecting spammers and fake profiles in social networks based on topology anomalies’, Human J., 2012, 1, (1), pp. 26–39.
    56. 56)
      • 26. Erdős, P., Rényi, A.: ‘On the evolution of random graphs’, Publ. Math. Inst. Hung. Acad. Sci, 1960, 5, (1), pp. 17–60.
    57. 57)
      • 21. Zhang, J., Zhang, R., Zhang, Y., et al: ‘On the impact of social botnets for spam distribution and digital-influence manipulation’. 2013 IEEE Conf. on Communications and Network Security (CNS), Washington, D.C., USA, 2013, pp. 46–54.
    58. 58)
      • 40. Boucher, V., Mourifié, I.: ‘My friend far far away: asymptotic properties of pairwise stable networks’. University of Toronto Working Paper, 2012, pp. 1–35.
    59. 59)
      • 38. Orbanz, P., Roy, D.M.: ‘Bayesian models of graphs, arrays and other exchangeable random structures’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 37, (2), pp. 437–461.
    60. 60)
      • 25. Moghaddam, S.H., Khademi, M., Abbaspour, M.: ‘Does homophily make socialbots more influential? exploring infiltration strategies’, arXiv preprint arXiv:1903.06827, 2019.
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