Detecting Malicious Websites by Query Templates

Kaneko, Satomi; Yamada, Akira; Sawaya, Yukiko; Thảo, Trần Phương; Kubota, Ayumu; Omote, Kazumasa

doi:10.1007/978-3-030-41025-4_5

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…The method proposed by Kaneko et al [10] named "Detecting Malicious Websites by Query Templates" used the machine learning algorithm DBSCAN to cluster malicious URLs and benign URLs. In the segmentation step, they chose a different way to divide URLs is that use all delimiters into URLs.…”

Section: The Segmentation Methods Of the Malicious Urls Detection Res...mentioning

confidence: 99%

An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection

Chen,

Omote

2024

Preprint

Self Cite

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Nowadays, machine learning is used in many fields. Not only in fields such as image recognition, machine learning is also used for malicious detection. Especially in recent years, there have been many studies using machine learning for malicious URL detection to replace traditional blacklists. In order to compare the performance of the malicious URLs detection method, researches used the F-score or other detection accuracy to evaluate, but there are some difficulties in evaluating the URL embedding method used in malicious URLs detection because the detection accuracy is also effect by machine learning or deep learning models and data sets. An evaluation method of URL embedding method that is not affected by other factors is particularly important. In this paper, we proposed an intrinsic evaluation method for URL embedding method that is not affected by machine learning models or deep learning models and data sets. Besides, We analyse some URL embedding methods according to intrinsic and extrinsic methods and offer a guidance in selecting suitable embedding methods in URL by analysing the results.

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Section: The Segmentation Methods Of the Malicious Urls Detection Res...mentioning

confidence: 99%

An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection

Chen,

Omote

2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The method proposed by Kaneko et al [3] named "Detecting Malicious Websites by Query Templates" used the machine learning algorithm DBSCAN to cluster malicious URLs and benign URLs. In the segmentation step, they chose a different way to divide URLs is that use all delimiters into URLs.…”

Section: Detecting Malicious Websites By Query Templatesmentioning

confidence: 99%

“…The first one, named the Alphabet method, means that splits the URL on a per-character basis and as a representative of the context-agnostic segmentation methods. The second one, Token method, means that as Kaneko's method [3], split the URL by all separators into URLs and as a representative of the contextconsidering segmentation methods. As mentioned above, refer to Yuan's segmentation method of URL structure, in order to keep the URL structure, we divided the URL into 5 parts with fixed lengths.…”

Section: Process Of Segmentationmentioning

confidence: 99%

“…For example, Le's [2] research used the CNN algorithm, Alphabet segmentation method, and Word segmentation method, with their own embedding method. Besides, Kaneko's [3] research used the DBSCAN algorithm and Token segmentation method without embedding method. Yuan's [4] research used many machine learning algorithms with splitting URLs by URL structure.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Based Three-Step Framework for Malicious URL Detection

Chen,

Omote

2023

Preprint

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In order to solve the shortcomings of using blacklist method to detect malicious URLs, such as slow update speed, the research of using machine learning to detect malicious URLs increasing. These research have proposed their own methods and obtained great accuracy, but the summary research on malicious URLs detection is insufficient. In this paper, we propose a three-step framework for malicious URLs detection, and we overview 14 related works by our three-step framework and find that almost all research on malicious URLs detection using machine learning can be classified by the three-step framework. We evaluate some machine learning models and context-considering methods and their suitability by our three-step framework. According to the results, we verify the importance of considering context and find that context-considering embedding methods are more important and the malicious URLs detection accuracy improved with context-considering methods.

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Toward the Establishment of Evaluating URL Embedding Methods Using Intrinsic Evaluator via Malicious URLs Detection

Chen,

Omote

2024

IFIP Advances in Information and Communication Technology

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Detecting Malicious Websites by Query Templates

Cited by 4 publications

References 6 publications

An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection

An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection

A Machine Learning Based Three-Step Framework for Malicious URL Detection

Toward the Establishment of Evaluating URL Embedding Methods Using Intrinsic Evaluator via Malicious URLs Detection

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