Phishing URL Identification Using Machine Learning, Ensemble Learning and Deep Learning Techniques

Prasanna, Keshava; Pradeepthi, K. V.; Saxena, Ashutosh

doi:10.1007/978-981-16-9705-0_56

Cited by 5 publications

(1 citation statement)

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“…One of the main reasons why users fall prey to phishing attacks is a lack of awareness of security tokens (attackers can also spoof tokens), a lack of attention to the site's URL, or an inattention to the anomalous behavior of the site's toolbar [3]. The authors of [4] proposed to identify the difference between real and phishing websites by analyzing the URL of the website where the random forest classifier was used. In [5], a real-time phishing protection system was proposed that uses seven different classification algorithms and functions based on natural language processing (NLP).…”

Section: Introductionmentioning

confidence: 99%

Solving the Problem of Detecting Phishing Websites Using Ensemble Learning Models

Kaibassova

Nurtay

Tau

et al. 2022

sjaitu

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Due to the popularity of the easiest way to obtain personal information among attackers, phishing detection is becoming a popular area for research aimed at countering the implementation of such attacks. Malicious website detection is essential to prevent the spread of malware and protect end users from victims. Unfortunately, malicious URL detection still needs to be better understood due to a lack of features and inaccurate classification. Possible sources were examined in order to investigate the subject. Based on the collected information from previous studies, this study is devoted to solving the problem of detecting phishing websites using Ensemble Learning. The aim of the work is to choose the most optimal algorithm for classifying phishing websites using gradient boosting algorithms. AdaBoost, CatBoost, and Gradient Boosting Classifier were chosen as Ensemble Learning algorithms and were used to improve the efficiency of classifiers. Practical studies of the parameters of each algorithm for finding the optimal classification model are given. Research and experiments were carried out on a dataset containing information extracted from the contents of a URL: main URL, domain, directory, and file. A thorough Exploratory Data Analysis (EDA) was carried out, as a result of which the main dependencies and patterns of determining phishing resources were identified using correlation analysis. ROC AUC Score was chosen as an evaluation metric for the algorithms. The best result for predicting phishing websites was demonstrated by the AdaBoost Classifier algorithm, with an average ROC AUC score of 99%. The results of the experiments were illustrated in the form of graphs and tables.

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Section: Introductionmentioning

confidence: 99%