Malicious URL Detection Based on Associative Classification

Kumi, Sandra; Lim, ChaeHo; Lee, Sang-Gon

doi:10.3390/e23020182

Cited by 34 publications

(14 citation statements)

References 12 publications

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“…Behaviorbased detection methods often seek ways to analyze anomalous behavior based on URL information. Some information is often applied to extract such as [1,3,4,5,6,7,8,9,10] domain, HTTP protocol, body, DNS query, lexical, host-based, etc. In this paper, we propose some new features representing abnormal behaviors of phishing URLs.…”

Section: Introduction Network Anomaly Detection Methods Are Usually Based Onmentioning

confidence: 99%

“…In the experimental part, the authors conducted experiments and proved that this proposal is superior to other methods using convolutional neural networks. In addition, the study [4] also proposed a detection method based on Associative Classification. Specifically, in this study, the author used some abnormal behavior of URLs including URL Features, Webpage Content Features.…”

Section: Related Workmentioning

confidence: 99%

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Developing a Framework for Detecting Phishing URLs using Machine Learning

Lam¹

2021

IJETAE

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The attack technique targeting end-users through phishing URLs is very dangerous nowadays. With this technique, attackers could steal user data or take control of the system, etc. Therefore, early detecting phishing URLs is essential. In this paper, we propose a method to detect phishing URLs based on supervised learning algorithms and abnormal behaviors from URLs. Finally, based on the research results, we build a framework for detecting phishing URLs through endusers. The novelty and advantage of our proposed method are that abnormal behaviors are extracted based on URLs which are monitored and collected directly from attack campaigns instead of using inefficient old datasets. Keywords— phishing URLs; detecting phishing URLs; abnormal behaviors of phishing URLs; Machine learning

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Section: Introduction Network Anomaly Detection Methods Are Usually Based Onmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Developing a Framework for Detecting Phishing URLs using Machine Learning

Lam¹

2021

IJETAE

View full text Add to dashboard Cite

show abstract

“…The best result achieved was by RF with 95% accuracy, 95.4% recall, and 95.3% precision. Along the same line, Kumi et al [ 7 ] proposed a method to classify URLs as either malicious or benign using a dataset collected by Manjeri et al [ 6 ]. The dataset was divided into 1,565 Benign URLs and 216 Malicious URLs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Precision is finding out how many of the classes predicted as positive are actually positive and is computed by (6) Count_-Recall is the ratio of classes predicted correctly out of all the positive classes and is computed by (7) below:…”

Section: Evaluation Criteriamentioning

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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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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“…The hybrid algorithm uses the strengths of both the rule induction algorithm (JRip) and the projective adaptive resonance theory (PART) algorithm to produce rule sets. Their dataset was collected from PhishTank [14], Yahoo, Alexa [16], CommonCrawl [52], and OpenPhish [36]. The total of extracted lexical, network-based, and content-based features was 40, with the proposed system returning the highest accuracy of 99.08%.…”

Section: ) Lexical and Content-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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Malicious URL Detection Based on Associative Classification

Cited by 34 publications

References 12 publications

Developing a Framework for Detecting Phishing URLs using Machine Learning

Developing a Framework for Detecting Phishing URLs using Machine Learning

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

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

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