Detecting Malicious URL

Alkhudair, Fatimah; Alassaf, Mada; Khan, Rehan Ullah; Alfarraj, Shouq

doi:10.1109/iccit-144147971.2020.9213792

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…The dataset employed was the Malicious and Benign Webpages Dataset, a publicly available dataset released by Singh and Kumar in 2020 [ 4 ]. The data were collected by crawling the Internet using the Mal Crawler tool [ 28 ], and the labels were verified (as good or bad) using Google Safe Browsing (API).…”

Section: Methodsmentioning

confidence: 99%

“…Taking into account the idea that features engineering plays a vital role in the performance of classifiers, our paper aims to extract different URL features, namely, lexical, network-based, and content-based features to help the classifier identify malicious URLs accurately. We apply these methods on a dataset constructed by Alkhudair et al [ 4 ], which to the best of our knowledge has not been used in the papers mentioned above to apply ML and DL classification models. Furthermore, we carry out a series of experiments to select the features selection technique that yields the best results with a high level of accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models

Aljabri

Alhaidari

Mohammad

et al. 2022

Computational Intelligence and Neuroscience

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The World Wide Web services are essential in our daily lives and are available to communities through Uniform Resource Locator (URL). Attackers utilize such means of communication and create malicious URLs to conduct fraudulent activities and deceive others by creating deceptive and misleading websites and domains. Such threats open the doors for many critical attacks such as spams, spyware, phishing, and malware. Therefore, detecting malicious URL is crucially important to prevent the occurrence of many cybercriminal activities. In this study, we examined a set of machine learning (ML) and deep learning (DL) models to detect malicious websites using a dataset comprising 66,506 records of URLs. We engineered three different types of features including lexical-based, network-based and content-based features. To extract the most discriminative features in the dataset, we applied several features selection algorithms, namely, correlation analysis, Analysis of Variance (ANOVA), and chi-square. Finally, we conducted a comparative performance evaluation for several ML and DL models considering set of criteria commonly used to evaluate such models. Results depicted that Naïve Bayes (NB) was the best model for detecting malicious URLs using the applied data with an accuracy of 96%. This research has made contribution to the field by conducting significant features engineering and analysis to identify the best features for malicious URLs predictions, compare different models and achieve a high accuracy using a large new URL dataset.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models

Aljabri

Alhaidari

Mohammad

et al. 2022

Computational Intelligence and Neuroscience

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“…Sandag et al [35] applied the k-NN method to website features and attained 95% accuracy. Alkhudair et al [36] and Panischev et al [37] proposed methods using RF to obtain 95% accuracy for both studies. Labhsetwar et al [38] achieved 92% accuracy by using an RF classifier.…”

Section: Related Workmentioning

confidence: 99%

A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection

Alanazi,

Gumaei

2023

Applied Sciences

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Malicious websites detection is one of the cyber-security tasks that protects sensitive information such as credit card details and login credentials from attackers. Machine learning (ML)-based methods have been commonly used in several applications of cyber-security research. Although there are some methods and approaches proposed in the state-of-the-art studies, the advancement of the most effective solution is still of research interest and needs to be improved. Recently, decision fusion methods play an important role in improving the accuracy of ML methods. They are broadly classified based on the type of fusion into a voting decision fusion technique and a divide and conquer decision fusion technique. In this paper, a decision fusion ensemble learning (DFEL) model is proposed based on voting technique for detecting malicious websites. It combines the predictions of three effective ensemble classifiers, namely, gradient boosting (GB) classifier, extreme gradient boosting (XGB) classifier, and random forest (RF) classifier. We use these classifiers because their advantages to perform well for class imbalanced and data with statistical noises such as in the case of malicious websites detection. A weighted majority-voting rule is utilized for generating the final decisions of used classifiers. The experimental results are conducted on a publicly available large dataset of malicious and benign websites. The comparative study exposed that the DFEL model achieves high accuracies, which are 97.25% on average of 10-fold cross-validation test and 98.50% on a holdout of 30% test set. This confirms the ability of proposed approach to improve the detection rate of malicious websites.

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“…Another study formulated by Zamir et al [24] proposed a framework to detect phishing websites using a stacking model. The used dataset is Kaggle [25]. They extracted 32 content, lexical, and network features.…”

Section: ) Lexical Content-based and Network-based Features Studiesmentioning

confidence: 99%

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

et al. 2022

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In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. As a result of attackers' inventive techniques, the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user's system and causing losses of billions of dollars each year. As a result, securing websites is becoming more critical. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. Moreover, due to the lack of studies related to malicious Arabic website detection, we highlight the directions of studies in this context. Finally, as a result of the analysis that we conducted on the selected studies, we present challenges that might degrade the quality of malicious URL detectors, along with possible solutions.

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Detecting Malicious URL

Cited by 9 publications

References 15 publications

An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models

An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models

A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

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