Impact of Social Engineering Attacks: A Literature Review

  • Conference paper
  • First Online:
Developments and Advances in Defense and Security

Abstract

Social engineering  is the practice, which allows attackers to obtain sensitive or confidential information froma user of a system or organization, exploiting specific characteristics of the human being. This is considered to be still one of the most threatening attacks within the digital world. The current study aims to explore social engineering attacks with significant impact. We conducted a systematic literature review from 2011 to 2020, applying the Barbara Kitchenham Methodological Guide. The main findings are concentrated in companies, financial institutions, and even vehicle vulnerabilities, which has caused economic losses and a decrease in the image and reputation loss damage of individuals and companies. Most of the causes are related to human behavior, such as innocence, unconsciousness, and lack of training or capacity. The primary victims are newly contracted workers, people with a certain lack of knowledge, celebrities, politicians, and middle and senior managers. Furthermore, social networks and e-mail are the primary sources from which attacks occur. Finally, we identified that Phishing and Ransomware are the most significant attacks on companies and individuals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 203.29
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 266.43
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 374.49
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. 2020 phishing statistics you need to know to protect your organization (2020), https://www.keepnetlabs.com/phishing-statistics-you-need-to-know-to-protect-your-organization/

  2. Algarni, A., Xu, Y., Chan, T., Tian, Y.-C.: Social engineering in social networking sites: affect-based model. In: 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013), pp. 508–515 (2013). https://doi.org/10.1109/ICITST.2013.6750253

  3. Algarni, A., Xu, Y., Chan, T.: Susceptibility to social engineering in social networking sites: the case of facebook. Presented at the (2015)

    Google Scholar 

  4. Algarni, A., Xu, Y., Chan, T.: In: Measuring Source Credibility of Social Engineering Attackers on Facebook, pp. 3686–3695. IEEE (2016)

    Google Scholar 

  5. Algarni, A., Xu, Y., Chan, T.: An empirical study on the susceptibility to social engineering in social networking sites: the case of facebook. Eur. J. Inf. Syst. 26(6), 661–687 (2017)

    Article  Google Scholar 

  6. Alotaibi, M.J., Furnell, S., Clarke, N.: A framework for reporting and dealing with end-user security policy compliance. Inf. Comput. Secur. (2019)

    Google Scholar 

  7. Beckers, K., Pape, S.: A Serious Game for Eliciting Social Engineering Security Requirements, pp. 16–25. IEEE (2016)

    Google Scholar 

  8. Benson, V., McAlaney, J., Frumkin, L.A.: Emerging threats for the human element and countermeasures in current cyber security landscape. In: Cyber Law, Privacy, and Security: Concepts, Methodologies, Tools, and Applications, pp. 1264–1269. IGI Global (2019)

    Google Scholar 

  9. Brown, S.D., Reavey, P.: False memories and real epistemic problems. Cult. Psychol. 23(2), 171–185 (2017). https://doi.org/10.1177/1354067X17695764

    Article  Google Scholar 

  10. Cole, S., Kvavilashvili, L.: Spontaneous and deliberate future thinking: a dual process account. Psychol. Res. (2019). https://doi.org/10.1007/s00426-019-01262-7

    Article  Google Scholar 

  11. Conteh, N.Y., Schmick, P.J.: Cybersecurity: risks, vulnerabilities and countermeasures to prevent social engineering attacks. Int. J. Adv. Comput. Res. 6(23), 31 (2016)

    Article  Google Scholar 

  12. Costantino, G., La Marra, A., Martinelli, F., Matteucci, I.: Candy: A Social Engineering Attack to Leak Information From Infotainment System, pp. 1–5. IEEE (2018)

    Google Scholar 

  13. Drew, J.M., Cross, C.: Fraud and its prey: conceptualising social engineering tactics and its impact on financial literacy outcomes. Presented at the Springer 2016

    Google Scholar 

  14. Edwards, M., Larson, R., Green, B., Rashid, A., Baron, A.: Panning for gold: Automatically analysing online social engineering attack surfaces. Comput. Secur. 69, 18–34 (2017)

    Article  Google Scholar 

  15. Ghafir, I., Saleem, J., Hammoudeh, M., Faour, H., Prenosil, V., Jaf, S., Jabbar, S., Baker, T.: Security threats to critical infrastructure: the human factor. J. Supercomput. 74(10), 4986–5002 (2018)

    Article  Google Scholar 

  16. Gong, N.Z., Liu, B.: Attribute inference attacks in online social networks. ACM Trans. Privacy Secur. (TOPS) 21(1), 1–30 (2018)

    Article  Google Scholar 

  17. Gupta, S., Singhal, A., Kapoor, A.: A Literature Survey on Social Engineering Attacks: Phishing Attack, pp. 537–540. IEEE (2016)

    Google Scholar 

  18. Hammour, R.A., Gharaibeh, Y.A., Qasaimeh, M., Al-Qassas, R.S.: The status of information security systems in banking sector from social engineering perspective. Presented at the (2019)

    Google Scholar 

  19. Irani, D., Balduzzi, M., Balzarotti, D., Kirda, E., Pu, C.: Reverse social engineering attacks in online social networks. Presented at the Springer 2011

    Google Scholar 

  20. Jaafor, O., Birregah, B.: Multi-layered Graph-Based Model for Social Engineering Vulnerability Assessment, pp. 1480–1488. IEEE (2015)

    Google Scholar 

  21. Jamil, A., Asif, K., Ghulam, Z., Nazir, M.K., Alam, S.M., Ashraf, R.: Mpmpa: A Mitigation and Prevention Model for Social Engineering Based Phishing Attacks on Facebook, pp. 5040–5048. IEEE (2018)

    Google Scholar 

  22. Joshi, C., Aliaga, J.R., Insua, D.R.: Insider threat modeling: an adversarial risk analysis approach. IEEE Trans. Inf. For. Secur. 16, 1131–1142 (2021). https://doi.org/10.1109/TIFS.2020.3029898

    Article  Google Scholar 

  23. Kaushalya, S., Randeniya, R., Liyanage, A.: An Overview of Social Engineering in the Context of Information Security, pp. 1–6. IEEE (2018)

    Google Scholar 

  24. Khan, N.F., Ikram, N.: Development of students’ security and privacy habits scale. Presented at the Springer 2019

    Google Scholar 

  25. Kitchenham, B., Charters, S.: Guidelines for performing systematic literature reviews in software engineering (2007)

    Google Scholar 

  26. Krombholz, K., Hobel, H., Huber, M., Weippl, E.: Social engineering attacks on the knowledge worker. Presented at the (2013)

    Google Scholar 

  27. Lancaster, K.: You may want to revise your cybersecurity plan after you see these 2020 ransomware statistics! (2020), https://www.idagent.com/blog/10-2020-ransomware-statistics-that-you-need-to-see/

  28. Luo, X., Brody, R., Seazzu, A., Burd, S.: Social engineering: the neglected human factor for information security management. Inf. Resour. Manage. J. (IRMJ) 24(3), 1–8 (2011)

    Article  Google Scholar 

  29. Meharchandani, D.: Staggering phishing statistics in 2020 (2020), https://www.kratikal.com/blog/staggering-phishing-statistics-in-2020/

  30. Mouton, F., Leenen, L., Venter, H.S.: Social engineering attack examples, templates and scenarios. Comput. Secur. 59, 186–209 (2016)

    Article  Google Scholar 

  31. Nelson, J., Lin, X., Chen, C., Iglesias, J., Li, J.: In: Social Engineering for Security Attacks, pp. 1–4. Data Science (2016)

    Google Scholar 

  32. Ovelgönne, M., Dumitraş, T., Prakash, B.A., Subrahmanian, V., Wang, B.: Understanding the relationship between human behavior and susceptibility to cyber attacks: a data-driven approach. ACM Trans. Intell. Syst. Technol. (TIST) 8(4), 1–25 (2017)

    Article  Google Scholar 

  33. Purplesec: 2020 ransomware statistics, data, & trends (dec 2020), https://purplesec.us/resources/cyber-security-statistics/ransomware/

  34. Rodríguez, G.E., Benavides, D.E., Torres, J., Flores, P., Fuertes, W.: Cookie scout: An analytic model for prevention of cross-site scripting (xss) using a cookie classifier. Presented at the Springer 2018

    Google Scholar 

  35. Salahdine, F., Kaabouch, N.: Social engineering attacks: a survey. Future Internet 11(4), 89 (2019)

    Article  Google Scholar 

  36. Saleem, J., Hammoudeh, M.: Defense methods against social engineering attacks. In: Computer and Network Security Essentials, pp. 603–618. Springer (2018)

    Google Scholar 

  37. Sawa, Y., Bhakta, R., Harris, I.G., Hadnagy, C.: Detection of Social Engineering Attacks Through Natural Language Processing of Conversations, pp. 262–265. IEEE (2016)

    Google Scholar 

  38. Sethi, R.J.: Spotting fake news: a social argumentation framework for scrutinizing alternative facts. Presented at the (2017). https://doi.org/10.1109/ICWS.2017.108

  39. Sumner, A., Yuan, X.: Mitigating phishing attacks: an overview. Presented at the (2019)

    Google Scholar 

  40. Wang, Z., Sun, L., Zhu, H.: Defining social engineering in cybersecurity. IEEE Access 8, 85094–85115 (2020)

    Article  Google Scholar 

  41. Wilson, B.: Introducing cyber security by designing mock social engineering attacks. J. Comput. Sci. Coll. 34(1), 235–241 (2018)

    Google Scholar 

  42. Yeboah-Boateng, E.O., Amanor, P.M.: Phishing, smishing & vishing: an assessment of threats against mobile devices. J. Emerg. Trends Comput. Inf. Sci. 5(4), 297–307 (2014)

    Google Scholar 

  43. Younis, Y.A., Musbah, M.: A framework to protect against phishing attacks. Presented at the (2020)

    Google Scholar 

  44. Zambrano, P., Torres, J., Tello-Oquendo, L., Jácome, R., Benalcázar, M.E., Andrade, R., Fuertes, W.: Technical map** of the grooming anatomy using machine learning paradigms: an information security approach. IEEE Access 7, 142129–142146 (2019). https://doi.org/10.1109/ACCESS.2019.2942805

    Article  Google Scholar 

Download references

Acknowledgements

We want to thank the resources granted for develo** the research project entitled Detection and Mitigation of Social Engineering attacks applying Cognitive Security. The authors would also like to thank the RED CEDIA’s financial support in the development of this study within the GT-Cybersecurity.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Walter Fuertes .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fuertes, W. et al. (2022). Impact of Social Engineering Attacks: A Literature Review. In: Rocha, Á., Fajardo-Toro, C.H., Rodríguez, J.M.R. (eds) Developments and Advances in Defense and Security . Smart Innovation, Systems and Technologies, vol 255. Springer, Singapore. https://doi.org/10.1007/978-981-16-4884-7_3

Download citation

Publish with us

Policies and ethics

Navigation