Malicious URL Detection: A Comparative Study

Shantanu, _; Janet, B.; Kumar, Ronit

doi:10.1109/icais50930.2021.9396014

Cited by 25 publications

(15 citation statements)

References 4 publications

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“…Butnaru et al [69] achieved a better result with an accuracy of 98.86% with their development of a phishing detection engine based on an ML model using nine features. Their dataset was formed from PhishTank [14] and Kaggle [70].…”

Section: ) Lexical and Network-based Features Studiesmentioning

confidence: 99%

“…However, Shantanu et al [71] achieved better accuracy of 99.7% with the same classifier in their comparison of the efficiency of several ML classifiers at detecting malicious URLs using 14 features. The dataset used was from the Kaggle repository [70]. All three studies extracted two types of features: network-based and lexical.…”

Section: ) Lexical and Network-based Features Studiesmentioning

confidence: 99%

“…A study by Afzal et al [124] introduced a hybrid DL approach named URLdeepDetect for time-of-click URL analysis and classification to detect malicious URLs using lexical features. This study used a dataset from PhishTank[14] and Kaggle [70]. The k-means model achieved the best results with an accuracy of 99.7%.…”

Section: ) Lexical Studiesmentioning

confidence: 99%

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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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Section: ) Lexical and Network-based Features Studiesmentioning

confidence: 99%

Section: ) Lexical and Network-based Features Studiesmentioning

confidence: 99%

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

et al. 2022

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“…However, the most common approach to identifying malicious domains is by means of machine learning (ML) and Deep Learning (DL) [11,14,20,23,24,27,28,[34][35][36][37][38][39][40][41][42]. Researchers can train ML algorithms to label URLs as malicious or benign using a set of extracted features.…”

Section: Related Workmentioning

confidence: 99%

Less Is More: Robust and Novel Features for Malicious Domain Detection

2022

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Malicious domains are increasingly common and pose a severe cybersecurity threat. Specifically, many types of current cyber attacks use URLs for attack communications (e.g., C&C, phishing, and spear-phishing). Despite the continuous progress in detecting cyber attacks, there are still critical weak spots in the structure of defense mechanisms. Since machine learning has become one of the most prominent malware detection methods, a robust feature selection mechanism is proposed that results in malicious domain detection models that are resistant to evasion attacks. This mechanism exhibits a high performance based on empirical data. This paper makes two main contributions: First, it provides an analysis of robust feature selection based on widely used features in the literature. Note that even though the feature set dimensional space is cut by half, the performance of the classifier is still improved (an increase in the model’s F1-score from 92.92% to 95.81%). Second, it introduces novel features that are robust with regard to the adversary’s manipulation. Based on an extensive evaluation of the different feature sets and commonly used classification models, this paper shows that models based on robust features are resistant to malicious perturbations and concurrently are helpful in classifying non-manipulated data.

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“…Demonstrate that NLP models may be used to detect website phishing using only URL strings, indicating that pre-trained transformers perform similarly to other approaches in phishing detection. Shantanu, Janet B, and Joshua Arul Kumar R [10], A lot of URLs have been used and misused to take advantage of a user's vulnerability. This study focuses on determining whether or not a URL is benign or harmful.…”

Section: Introductionmentioning

confidence: 99%

Malicious Websites Classification Using Machine Learning Techniques: A Survey Paper

Shilpa¹

2022

IJRASET

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With the rapid rise of online development, Malware detection is critical for determining if a URL is hazardous or not because hackers steal user information such as usernames, passwords, and credit card numbers by impersonating a trustworthy entity via the internet and use it for illegal activities without the user's knowledge. As a result of applying many classifiers to detect URLs and conducting some operations, the best classifier was chosen as having a good performance in detecting URLs as malicious or benign.

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Malicious URL Detection: A Comparative Study

Cited by 25 publications

References 4 publications

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

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

Less Is More: Robust and Novel Features for Malicious Domain Detection

Malicious Websites Classification Using Machine Learning Techniques: A Survey Paper

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