SpringerLink
Log in

Prevention of Phishing Attack in Internet-of-Things based Cyber-Physical Human System

  • Chapter
  • First Online:
High Performance Vision Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 913))

Abstract

In Internet-of-Things enabled Cyber-Physical Human System (CPHS), the controller control the destination systems. The challenger or hacker can perform a number of attacks on this network to threaten the identity and vulnerability of the system, by consuming the networked resources. One of the issue that possesses threat on identities and user credentials is the phishing. The mechanisms for phishing detection in IoT based CPHS should be light-weight and not much complicated in order to meet the CPHS requirement. In CPHS, the credentials can be compromised from the user by showing very similar electronic pages or messages, and encouraging user to provide their secured financial data. These issues need to be resolved in order to get the right output and get all the functionalities to work properly. CPHS has mainly two major components, the first one is controller and second one is destination system. Commands are sent from the sensor to the destination via sensor nodes on the network and the destination system communicates with the controller about what actions to perform or how to deal with the information that controller has sent.

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

Access this chapter

Log in via an institution

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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • 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. T.N. Jagatic, N.A. Johnson, M. Jakobsson, F. Menczer, Social phishing. Commun ACM 50(10), 94–100 (2007)

    Google Scholar 

  2. P. Kumaraguru, Y. Rhee, S. Sheng, S. Hasan, A. Acquisti, L.F. Cranor, J. Hong, Teaching Johnny not to fall for phish. ACM Trans. Internet Technol. (TOIT) 10(2), 7:1–7:31 (2010)

    Google Scholar 

  3. I. Khalil, S. Bagchi, N. Shroff, Analysis and evaluation of Secos, a protocol for energy efficient and secure communication in sensor networks. Ad Hoc Netw. 5(3), 360–391 (2007)

    Article  Google Scholar 

  4. G. Varshney, M. Misra, P.K. Atrey, A survey and classification of web phishing detection schemes. Secur. Commun. Netw. 9(18), 6266–6284 (2016)

    Article  Google Scholar 

  5. J. Hong, The state of phishing attacks. Commun. ACM 55(1), 74–81 (2012)

    Article  Google Scholar 

  6. K.L. Chiew, K.S.C. Yong, C.L. Tan, A survey of phishing attacks: their types, vectors and technical approaches. Expert Syst. Appl. 106, 1–20 (2018)

    Article  Google Scholar 

  7. H. Huang, J. Tan, and L. Liu, Countermeasure techniques for deceptive phishing attack, in Proceedings of International Conference on New Trends in Information and Service Science, pp. 636–641 (2009)

    Google Scholar 

  8. B. Parmar, Protecting against spear-phishing. Comput. Fraud. Secur. 2012(1), 8–11 (2012)

    Article  Google Scholar 

  9. C. Karlof, U. Shankar, J.D. Tygar, D. Wagner, Dynamic pharming attacks and locked same-origin policies for web browsers, in Proceedings of the 14th ACM Conference on Computer and Communications Security, pp. 58–71 (2007)

    Google Scholar 

  10. B.B. Gupta, N.A.G. Arachchilage, K.E. Psannis, Defending against phishing attacks: taxonomy of methods, current issues and future directions. Telecommun. Syst. 67(2), 247–267 (2018)

    Google Scholar 

  11. N. Chou, R. Ledesma, Y. Teraguchi, J.C. Mitchell, Client-side defense against web-based identity theft, in Proceedings of the Network and Distributed System Security Symposium, NDSS 2004, pp. 01–16 (2004)

    Google Scholar 

  12. T. Raffetseder, E. Kirda, C. Kruegel, Building anti-phishing browser plug-ins: an experience report, in Proceedings of the 3rd International Workshop on Software Engineering for Secure Systems, pp. 1–6 (2007)

    Google Scholar 

  13. A. Stone, Natural-language processing for intrusion detection. Computer 40(12), 103–105 (2007)

    Article  Google Scholar 

  14. M. Sharifi, S.H. Siadati, A phishing sites blacklist generator, in 2008 IEEE/ACS International Conference on Computer Systems and Applications, pp. 840–843 (2008)

    Google Scholar 

  15. D.L. Cook, V.K. Gurbani, M. Daniluk, Phishwish: a simple and stateless phishing filter. Secur. Commun. Netw. 2(1), 29–43 (2008)

    Article  Google Scholar 

  16. Y. Joshi, S. Saklikar, D. Das, S. Saha, PhishGuard: a browser plug-in for protection from phishing, in 2008 2nd International Conference on Internet Multimedia Services Architecture and Applications, pp. 1–6 (2008)

    Google Scholar 

  17. M. Hara, A. Yamada, Y. Miyake, Visual similarity-based phishing detection without victim site information, in IEEE Symposium on Computational Intelligence in Cyber Security, pp. 30–36 (2009)

    Google Scholar 

  18. C. Yue, H. Wang. BogusBiter: a transparent protection against phishing attacks. ACM Trans. Internet Technol. 10(2), 6:1–6:31 (2010)

    Google Scholar 

  19. P. Prakash, M. Kumar, R.R. Kompella, M. Gupta, PhishNet: predictive blacklisting to detect phishing attacks, in Proceedings IEEE INFOCOM, pp. 1 – 5 (2010)

    Google Scholar 

  20. A. Bergholz, J. De Beer, S. Glahn, M.-F. Moens, G. Paaß, S. Strobel, New filtering approaches for phishing email. J. Comput. Secur. 18(1), 7–35 (2010)

    Article  Google Scholar 

  21. C. Whittaker, B. Ryner, M. Nazif, Large-scale automatic classification of phishing pages, in Proceedings of the Network and Distributed System Security Symposium, NDSS 2010, pp. 01–14 (2010)

    Google Scholar 

  22. G. Liu, B. Qiu, L. Wenyin, Automatic detection of phishing target from phishing webpage, in 20th International Conference on Pattern Recognition, pp. 4153–4156 (2010)

    Google Scholar 

  23. B. Wardman, T. Stallings, G. Warner, A. Skjellum, High-performance content-based phishing attack detection, in Proceedings of eCrime Researchers Summit, pp. 1–9 (2011)

    Google Scholar 

  24. S. Afroz, R. Greenstadt, PhishZoo: detecting phishing websites by looking at them, in 2011 IEEE 5th International Conference on Semantic Computing, pp. 368–375 (2011)

    Google Scholar 

  25. H. Kim, J.H. Huh, Detecting DNS-poisoning-based phishing attacks from their network performance characteristics. Electron. Lett. 47(11), 656–658 (2011)

    Article  Google Scholar 

  26. H. Zhang, G. Liu, T.W.S. Chow, W. Liu, Textual and visual content-based anti-phishing: a bayesian approach. IEEE Trans. Neural Netw. 22(10), 1532–1546 (2011)

    Article  Google Scholar 

  27. E.H. Chang, K.L. Chiew, S.N. Sze, W.K. Tiong, Phishing detection via identification of website identity, in 2013 International Conference on IT Convergence and Security (ICITCS), pp. 1–4 (2013)

    Google Scholar 

  28. T.-C. Chen, T. Stepan, S. Dick, J. Miller, An anti-phishing system employing diffused information. ACM Trans. Inf. Syst. Secur. 16(4), 16:1–16:31 (2014)

    Google Scholar 

  29. N.M. Shekokar, C. Shah, M. Mahajan, S. Rachh, An ideal approach for detection and prevention of phishing attacks. Procedia Comput. Sci. 49, 82–91 (2015); Proceedings of 4th International Conference on Advances in Computing, Communication and Control (ICAC3’15)

    Google Scholar 

  30. G. Ramesh, J. Gupta, P.G. Gamya, Identification of phishing webpages and its target domains by analyzing the feign relationship. J. Inf. Secur. Appl. 35, 75–84 (2017)

    Google Scholar 

  31. Y. Zhang, J.I. Hong, L.F. Cranor, Cantina: a content-based approach to detecting phishing web sites, in Proceedings of the 16th International Conference on World Wide Web, pp. 639–648 (2007)

    Google Scholar 

  32. E. Medvet, E. Kirda, C. Kruegel, Visual-similarity-based phishing detection, in Proceedings of the 4th International Conference on Security and Privacy in Communication Netowrks, pp. 22:1–22:6 (2008)

    Google Scholar 

  33. K.-T. Chen, J.-Y. Chen, C.-R. Huang, C.-S. Chen, Fighting phishing with discriminative keypoint features. IEEE Internet Comput. 13(3), 56–63 (2009)

    Article  Google Scholar 

  34. A. Blum, B. Wardman, T. Solorio, G. Warner, Lexical feature based phishing url detection using online learning, in Proceedings of the 3rd ACM Workshop on Artificial Intelligence and Security, pp 54–60 (2010)

    Google Scholar 

  35. G. **ang, J. Hong, C.P. Rose, L. Cranor. CANTINA+: a feature-rich machine learning framework for detecting phishing web sites. ACM Trans. Inf. Syst. Secur. (TISSEC) 14(2), 21:1–21:28 (2011)

    Google Scholar 

  36. Y. Zhou, Y. Zhang, J. **ao, Y. Wang, W. Lin, Visual similarity based anti-phishing with the combination of local and global features, in 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, pp 189–196 (2014)

    Google Scholar 

  37. C.L. Tan, K.L. Chiew, K. Wong, S.N. Sze, PhishWHO: phishing webpage detection via identity keywords extraction and target domain name finder. Decis. Support Syst. 88, 18–27 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology Jamshedpur, Jamshedpur, India

    Alekha Kumar Mishra

  2. School of Information Technology and Engineering, VIT Vellore, Vellore, India

    Asis Kumar Tripathy

  3. School of Computer Science and Engineering, VIT Vellore, Vellore, India

    Sowmya Saraswathi & Meenakshi Das

Authors
  1. Alekha Kumar Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asis Kumar Tripathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sowmya Saraswathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meenakshi Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Asis Kumar Tripathy .

Editor information

Editors and Affiliations

  1. Jaypee Institute of Information Technology, Noida, India

    Aparajita Nanda

  2. Jaypee Institute of Information Technology, Noida, India

    Nisha Chaurasia

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mishra, A.K., Tripathy, A.K., Saraswathi, S., Das, M. (2020). Prevention of Phishing Attack in Internet-of-Things based Cyber-Physical Human System. In: Nanda, A., Chaurasia, N. (eds) High Performance Vision Intelligence. Studies in Computational Intelligence, vol 913. Springer, Singapore. https://doi.org/10.1007/978-981-15-6844-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-6844-2_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6843-5

  • Online ISBN: 978-981-15-6844-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation