Algorithm Evaluation for Classification “Phishing Website” Using Several Classification Algorithms

Wahyudi, Rizki; Marcos, Hendra; Hasanah, Uswatun; Hartato, Bambang Pilu; Astuti, Tri; Prasetyo, Rizal Anjas

doi:10.1109/icitisee.2018.8720975

Cited by 3 publications

(1 citation statement)

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“…This paper is focused especially on the collection of phishing data, the data mining step of knowledge discovery and the creation of a Chrome plugin for data collection and phishing detection. The interest of academics in the data mining step for the purposes of phishing detection has been shown in several papers [5], [9], [14]. One of the most popular algorithms for the data mining step is the C4.5 algorithm creating an easily interpretable decision tree for classification [15], [7].…”

Section: Introductionmentioning

confidence: 99%

Data Mining-Based Phishing Detection

Boháčik

Skula

Zábovský

2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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Webpages can be faked easily nowadays and as there are many internet users, it is not hard to find some becoming victims of them. Simultaneously, it is not uncommon these days that more and more activities such as banking and shopping are being moved to the internet, which may lead to huge financial losses. In this paper, a developed Chrome plugin for data mining-based detection of phishing webpages is described. The plugin is written in JavaScript and it uses a C4.5 decision tree model created on the basis of collected data with eight describing attributes. The usability of the model is validated with 10-fold cross-validation and the computation of sensitivity, specificity and overall accuracy. The achieved results of experiments are promising.

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