Detecting Phishing Websites Using Rule-Based Classification Algorithm: A Comparison

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 9)


In today’s time, phishy website detection is one of the important challenges in the field of information security due to the large numbers of online transactions going through over the websites. Website phishing means stealing one’s personal information over the Internet such as system backup data, user login credentials, bank account details or other security information. Phishing means creation of phishy or fake websites which look like legitimate ones. In this research paper, we use the associative classification data mining approach that is also named as rule-based classification technique by which we can detect a phishy website and thereby identifying the better detection algorithm which has a higher accuracy detection rate. The algorithms used are Naïve Bayes and PART algorithms of associative classification data mining approach. Moreover, we classify the websites into a legitimate website or a phishy website from the collected datasets of websites. The implementation will be done on the datasets of 1,353 websites which contain phishy sites as well as legitimate sites. At the end, results will show us the higher accuracy detection rate algorithm, which will more correctly identify phishing or legitimate websites.


Phishy website Naïve Bayes algorithm PART algorithm 


  1. 1.
    Abdelhamid N, Ayesh A, Thabtah F (2014) Phishing detection based associative classification data mining. Expert Syst Appl 41. ElsevierGoogle Scholar
  2. 2.
    Mahajan A, Ganpati A (2014) Performance evaluation of rule based classification algorithms. Int J Ad Res Comput Eng Technol (IJARCET)Google Scholar
  3. 3.
    Taalohi M, Langari N, Tabatabaee H (2015) Identifying phishing websites by techniques hyper heuristic and machine learning. ISSN Sci Int. LahoreGoogle Scholar
  4. 4.
    Datasets of phishing and legitimate websites from the sites as Phishtank, Millersmiles, and UCI machine learning repository site.
  5. 5.
  6. 6.
    Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu SY, Zhou ZH, Steinbach M, Hand DJ, Steinberg D (2007) Top 10 Algorithm in data mining. Springer Verlag London Limited publishedGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Sudhanshu Gautam
    • 1
  • Kritika Rani
    • 1
  • Bansidhar Joshi
    • 1
  1. 1.Department of Computer Science and EngineeringJaypee Institute of Information TechnologyNoidaIndia

Personalised recommendations