Intelligent Rule based Phishing Websites Classification

Article English OPEN
Mohammad, Rami ; McCluskey, T.L. ; Thabtah, Fadi Abdeljaber (2014)
  • Publisher: Institution of Engineering and Technology
  • Related identifiers: doi: 10.1049/iet-ifs.2013.0202
  • Subject: TN
    acm: ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS

Phishing is described as the art of emulating a website of a creditable firm intending to grab user’s private information such as usernames, passwords and social security number. Phishing websites comprise a variety of cues within its content-parts as well as browser-based security indicators. Several solutions have been proposed to tackle phishing. Nevertheless, there is no single magic bullet that can solve this threat radically. One of the promising techniques that can be used in predicting phishing attacks is based on data mining. Particularly the “induction of classification rules”, since anti-phishing solutions aim to predict the website type accurately and these exactly fit the classification data mining. In this paper, we shed light on the important features that distinguish phishing websites from legitimate ones and assess how rule-based classification data mining techniques are applicable in predicting phishing websites. We also experimentally show the ideal rule based classification technique for detecting phishing.
  • References (29)
    29 references, page 1 of 3

    2. Sophie GP, Gustavo GG, Maryline L. Decisive Heuristics to Differentiate Legitimate from Phishing Sites. In 2011 Conference on Network and Information Systems Security; 2011: IEEE. p. 1-9.

    3. Guang X, Jason o, Carolyn P R, Lorrie C. CANTINA+: A Feature-rich Machine Learning Framework for Detecting Phishing Web Sites. ACM Transactions on Information and System Security. 2011 Sep: p. 1-28.

    4. Witten IH, Frank E. Data mining: practical machine learning tools and techniques with Java implementations. New York, NY, USA:; March 2002.

    5. H DJ. Rule induction-machine learning techniques. Computing & Control Engineering Journal. 1994 October: p. 249-255.

    6. Gartner, Inc. [Online]. Available from: http://www.gartner.com/technology/home.jsp.

    7. Lennon, M. Security Week. [Online].; 2011. Available from: http://www.securityweek.com/cisco-targeted-attacks-costorganizations-129-billion-annually.

    8. Aburrous, M , Hossain, M. A. , Dahal, K. , Fadi, T. Predicting Phishing Websites using Classification Mining Techniques. In Seventh International Conference on Information Technology.; 2010; Las Vegas, Nevada, USA.: IEEE. p. 176-181.

    9. Thabtah F, Peter C, Peng Y. MCAR: Multi-class Classification based on Association Rule. In The 3rd ACS/IEEE International Conference on Computer Systems and Applications; 2005. p. 33.

    10. Hu K, Lu Y, Zhou L, Shi C. Integrating Classification and association rule Mining. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98, Plenary Presentation); 1998; New York, USA: Springer-Verlag. p. 443 - 447.

    11. Quinlan JR. Improved use of continuous attributes in c4.5. Journal of Artificial Intelligence Research. 1996;: p. 77-90.

  • Similar Research Results (6)
  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    1,061
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    University of Huddersfield Repository - IRUS-UK 0 1,061
Share - Bookmark