Phish webpage classification using hybrid algorithm of machine learning and statistical induction ratios
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017132" target="_blank" >RIV/62690094:18450/20:50017132 - isvavai.cz</a>
Result on the web
<a href="http://www.inderscience.com/offer.php?id=108727" target="_blank" >http://www.inderscience.com/offer.php?id=108727</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1504/IJDMMM.2020.108727" target="_blank" >10.1504/IJDMMM.2020.108727</a>
Alternative languages
Result language
angličtina
Original language name
Phish webpage classification using hybrid algorithm of machine learning and statistical induction ratios
Original language description
Although the conventional machine learning-based anti-phishing techniques outperform their competitors in phishing detection, they are still targeted by zero-hour phish webpages due to their constraints of phishing induction. Therefore, phishing induction must be boosted up with the extraction of new features, the selection of robust subsets of decisive features, the active learning of classifiers on a big webpage stream. In this paper, we propose a hybrid feature-based classification algorithm (HFBC) for decisive phish webpage classification. HFBC hybridises two statistical criteria optimised feature occurrence (OFC) and phishing induction ratio (PIR) with the induction settings of the most salient machine learning algorithms, Naive bays and decision tree. Additionally, we propose two constituent algorithms of features extraction and features selection for holistic phish webpage characterisation. The superiority of our proposed approach is justified and proven throughout chronological, real-time, and comparative analyses against existing machines learning-based anti-phishing techniques.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT
ISSN
1759-1163
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
3
Country of publishing house
CH - SWITZERLAND
Number of pages
22
Pages from-to
255-276
UT code for WoS article
000556833300001
EID of the result in the Scopus database
2-s2.0-85084789086