Phish webpage classification using hybrid algorithm of machine learning and statistical induction ratios

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Phish webpage classification using hybrid algorithm of machine learning and statistical induction ratios

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Phish webpage classification using hybrid algorithm of machine learning and statistical induction ratios

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT

ISSN

1759-1163

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

22

Strana od-do

255-276

Kód UT WoS článku

000556833300001

EID výsledku v databázi Scopus

2-s2.0-85084789086