Research on Malicious URL Identification and Analysis for Network Security

Huang, Zhuofan; Zhang, Yangsen; Duan, Ruixue; Wang, Renjie

doi:10.1109/ic-nidc54101.2021.9660440

Cited by 2 publications

(1 citation statement)

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“…The authors of [6] consider the effectiveness and suitability of using methods to detect phishing messages even before the end user reads the letter. Also, the vector representation of URLs was studied in [7]. In general, machine learning techniques have been used to classify web pages by systematically analyzing a set of features that reflect the characteristics of a malicious web page [8].…”

Section: Introductionmentioning

confidence: 99%

Solving the Problem of Detecting Phishing Websites Using Ensemble Learning Models

Kaibassova

Nurtay

Tau

et al. 2022

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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%