Skip to main content

CatchPhish: detection of phishing websites by inspecting URLs


There exists many anti-phishing techniques which use source code-based features and third party services to detect the phishing sites. These techniques have some limitations and one of them is that they fail to handle drive-by-downloads. They also use third-party services for the detection of phishing URLs which delay the classification process. Hence, in this paper, we propose a light-weight application, CatchPhish which predicts the URL legitimacy without visiting the website. The proposed technique uses hostname, full URL, Term Frequency-Inverse Document Frequency (TF-IDF) features and phish-hinted words from the suspicious URL for the classification using the Random forest classifier. The proposed model with only TF-IDF features on our dataset achieved an accuracy of 93.25%. Experiment with TF-IDF and hand-crafted features achieved a significant accuracy of 94.26% on our dataset and an accuracy of 98.25%, 97.49% on benchmark datasets which is much better than the existing baseline models.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

  2. 2.


  1. Abutair H, Belghith A, AlAhmadi S (2018) CBR-PDS: a case-based reasoning phishing detection system. J Ambient Intell Hum Comput.

    Article  Google Scholar 

  2. APWG (2018) Phishing attack trends reports, 1st quarter 2018. Accessed 20 Sept 2018

  3. Bottazzi G, Casalicchio E, Cingolani D, Marturana F, Piu M (2015) Mp-shield: a framework for phishing detection in mobile devices. In: Computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing (CIT/IUCC/DASC/PICOM), 2015 IEEE international conference on, IEEE, pp 1977–1983

  4. Britt J, Wardman B, Sprague A, Warner G (2012) Clustering potential phishing websites using DeepMD5. In: LEET

  5. Chiew KL, Chang EH, Tiong WK et al (2015) Utilisation of website logo for phishing detection. Comput Secur 54:16–26.

    Article  Google Scholar 

  6. Chiew KL, Choo JSF, Sze SN, Yong KS (2018) Leverage website favicon to detect phishing websites. Secur Commun Netw 78:95.

    Article  Google Scholar 

  7. Chiew KL, Tan CL, Wong K, Yong KS, Tiong WK (2019) A new hybrid ensemble feature selection framework for machine learning based phishing detection system. Inf Sci 484:153–166.

    Article  Google Scholar 

  8. Choi H, Zhu BB, Lee H (2011) Detecting malicious web links and identifying their attack types. WebApps 11:11–11

    Google Scholar 

  9. Chou N, Ledesma R, Teraguchi Y, Boneh D, Mitchell JC (2004) Client-side defense against web-based identity theft. Computer Science Department, Stanford University.

  10. Chu W, Zhu BB, Xue F, Guan X, Cai Z (2013) Protect sensitive sites from phishing attacks using features extractable from inaccessible phishing URLs. In: Communications (ICC), 2013 IEEE international conference on, IEEE, pp 1990–1994

  11. Dhamija R, Tygar JD, Hearst M (2006) Why phishing works. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, pp 581–590.

  12. Felegyhazi M, Kreibich C, Paxson V (2010) On the potential of proactive domain blacklisting. LEET 10:6–6

    Google Scholar 

  13. Feng F, Zhou Q, Shen Z, Yang X, Han L, Wang J (2018) The application of a novel neural network in the detection of phishing websites. J Ambient Intell Hum Comput.

    Article  Google Scholar 

  14. Garera S, Provos N, Chew M, Rubin AD (2007) A framework for detection and measurement of phishing attacks. In: Proceedings of the 2007 ACM workshop on recurring malcode, ACM, pp 1–8

  15. Gastellier-Prevost S, Granadillo GG, Laurent M (2011) Decisive heuristics to differentiate legitimate from phishing sites. In: Network and information systems security (SAR-SSI), 2011 conference on, IEEE, pp 1–9

  16. Gowtham R, Krishnamurthi I (2014) A comprehensive and efficacious architecture for detecting phishing webpages. Comput Secur 40:23–37.

    Article  Google Scholar 

  17. Han W, Cao Y, Bertino E, Yong J (2012) Using automated individual white-list to protect web digital identities. Expert Syst Appl 39(15):11861–11869

    Article  Google Scholar 

  18. Hara M, Yamada A, Miyake Y (2009) Visual similarity-based phishing detection without victim site information. In: Computational intelligence in cyber security, 2009. CICS’09. IEEE symposium on, IEEE, pp 30–36.

  19. He M, Horng SJ, Fan P, Khan MK, Run RS, Lai JL, Chen RJ, Sutanto A (2011) An efficient phishing webpage detector. Expert Syst Appl 38(10):12018–12027.

    Article  Google Scholar 

  20. Huang H, Qian L, Wang Y (2012) A SVM-based technique to detect phishing URLs. Inf Technol J 11(7):921–925

    Article  Google Scholar 

  21. Jain AK, Gupta BB (2017) Two-level authentication approach to protect from phishing attacks in real time. J Ambient Intell Hum Comput.

    Article  Google Scholar 

  22. Jain AK, Gupta BB (2018) A machine learning based approach for phishing detection using hyperlinks information. J Ambient Intell Hum Comput.

    Article  Google Scholar 

  23. KasperskyLab (2017) Kaspersky lab:spam and phishing report 2017. Accessed 20 Sept 2018

  24. Lin MS, Chiu CY, Lee YJ, Pao HK (2013) Malicious URL filtering—a big data application. In: Big data, 2013 IEEE international conference on, IEEE, pp 589–596

  25. Marchal S, François J, State R, Engel T (2014) Phishstorm: detecting phishing with streaming analytics. IEEE Trans Netw Serv Manag 11(4):458–471

    Article  Google Scholar 

  26. Marchal S, Saari K, Singh N, Asokan N (2016) Know your phish: novel techniques for detecting phishing sites and their targets. In: Distributed computing systems (ICDCS), 2016 IEEE 36th international conference on, IEEE, pp 323–333

  27. Marchal S, Armano G, Gröndahl T, Saari K, Singh N, Asokan N (2017) Off-the-Hook: an efficient and usable client-side phishing prevention application. IEEE Trans Comput 66(10):1717–1733

    MathSciNet  Article  Google Scholar 

  28. Moghimi M, Varjani AY (2016) New rule-based phishing detection method. Expert Syst Appl 53:231–242.

    Article  Google Scholar 

  29. Mohammad RM, Thabtah F, McCluskey L (2012) An assessment of features related to phishing websites using an automated technique. In: Internet technology and secured transactions, 2012 international conference for, IEEE, pp 492–497

  30. Mohammad RM, Thabtah F, McCluskey L (2014) Predicting phishing websites based on self-structuring neural network. Neural Comput Appl 25(2):443–458

    Article  Google Scholar 

  31. Mohammad RM, Thabtah F, McCluskey L (2015) Tutorial and critical analysis of phishing websites methods. Comput Sci Rev 17:1–24

    MathSciNet  Article  Google Scholar 

  32. Patil DR, Patil J (2018) Malicious URLs detection using decision tree classifiers and majority voting technique. Cybern Inf Technol 18(1):11–29

    Google Scholar 

  33. Prakash P, Kumar M, Kompella RR, Gupta M (2010) Phishnet: predictive blacklisting to detect phishing attacks. In: INFOCOM, 2010 proceedings IEEE, IEEE, pp 1–5.

  34. Ramesh G, Krishnamurthi I, Kumar KSS (2014) An efficacious method for detecting phishing webpages through target domain identification. Decis Support Syst 61:12–22.

    Article  Google Scholar 

  35. Ranganayakulu D, Chellappan C (2013) Detecting malicious URLs in e-mail-an implementation. AASRI Proced 4:125–131

    Article  Google Scholar 

  36. Rao RS, Pais AR (2017) An enhanced blacklist method to detect phishing websites. In: International conference on information systems security, Springer, pp 323–333

  37. Rao RS, Pais AR (2018) Detection of phishing websites using an efficient feature-based machine learning framework. Neural Comput Appl.

    Article  Google Scholar 

  38. Rosiello AP, Kirda E, Ferrandi F et al (2007) A layout-similarity-based approach for detecting phishing pages. In: Security and privacy in communications networks and the workshops, 2007. SecureComm 2007. Third international conference on, IEEE, pp 454–463

  39. RSA (2018) RSA-online-fraud-report q1 2018. Accessed 20 Sept 2018

  40. Sahingoz OK, Buber E, Demir O, Diri B (2018) Machine learning based phishing detection from URLs. Expert Syst Appl 117:345

    Article  Google Scholar 

  41. Salton G, McGill MJ (1986) Introduction to modern information retrieval. McGraw-Hill, New York

    MATH  Google Scholar 

  42. Shirazi H, Haefner K, Ray I (2017) Fresh-phish: a framework for auto-detection of phishing websites. In: Information reuse and integration (IRI), 2017 IEEE international conference on, IEEE, pp 137–143

  43. Shirazi H, Bezawada B, Ray I (2018) Kn0w thy doma1n name: unbiased phishing detection using domain name based features. In: Proceedings of the 23nd ACM on symposium on access control models and technologies, ACM, pp 69–75

  44. Su KW, Wu KP, Lee HM, Wei TE (2013) Suspicious URL filtering based on logistic regression with multi-view analysis. In: Information security (Asia JCIS), 2013 eighth Asia joint conference on, IEEE, pp 77–84

  45. Symantec (2018) Internet security threat report, 2018. Accessed 20 Sept 2018

  46. Tan CL, Chiew KL, Wong K, Sze SN (2016) Phishwho: phishing webpage detection via identity keywords extraction and target domain name finder. Decis Support Syst 88:18–27.

    Article  Google Scholar 

  47. Thomas K, Grier C, Ma J, Paxson V, Song D (2011) Design and evaluation of a real-time URL spam filtering service. In: Security and privacy (SP), 2011 IEEE symposium on, IEEE, pp 447–462

  48. Varshney G, Misra M, Atrey PK (2016) A phish detector using lightweight search features. Comput Secur 62:213–228.

    Article  Google Scholar 

  49. Verma R, Dyer K (2015) On the character of phishing URLs: accurate and robust statistical learning classifiers. In: Proceedings of the 5th ACM conference on data and application security and privacy, ACM, pp 111–122

  50. Wang W, Shirley K (2015) Breaking bad: detecting malicious domains using word segmentation. arXiv preprint arXiv:150604111

  51. Wang Y, Agrawal R, Choi BY (2008) Light weight anti-phishing with user whitelisting in a web browser. In: Region 5 conference, 2008 IEEE, IEEE, pp 1–4

  52. Xiang G, Hong JI (2009) A hybrid phish detection approach by identity discovery and keywords retrieval. In: Proceedings of the 18th international conference on world wide web, ACM, pp 571–580

  53. Xiang G, Hong J, Rose CP, Cranor L (2011) Cantina+: a feature-rich machine learning framework for detecting phishing web sites. ACM Trans Inf Syst Secur (TISSEC) 14(2):21.

    Article  Google Scholar 

  54. Xu L, Zhan Z, Xu S, Ye K (2013) Cross-layer detection of malicious websites. In: Proceedings of the third ACM conference on data and application security and privacy, ACM, pp 141–152

  55. Zhang D, Yan Z, Jiang H, Kim T (2014) A domain-feature enhanced classification model for the detection of Chinese phishing e-business websites. Inf Manag 51(7):845–853.

    Article  Google Scholar 

  56. Zhang Y, Hong JI, Cranor LF (2007) Cantina: a content-based approach to detecting phishing web sites. In: Proceedings of the 16th international conference on World Wide Web, ACM, pp 639–648.

  57. Zuhair H, Selamat A, Salleh M (2016) New hybrid features for phish website prediction. Int J Adv Soft Comput Appl 8(1):745

    Google Scholar 

Download references


The authors would like to thank Ministry of Electronics and Information Technology (Meity), Government of India for their support in part of the research.

Author information



Corresponding author

Correspondence to Routhu Srinivasa Rao.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rao, R.S., Vaishnavi, T. & Pais, A.R. CatchPhish: detection of phishing websites by inspecting URLs. J Ambient Intell Human Comput 11, 813–825 (2020).

Download citation


  • URL
  • Phishing
  • Anti-phishing
  • TF-IDF
  • Hostname
  • Random forest