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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Toward the Establishment of Evaluating URL Embedding Methods Using Intrinsic Evaluator via Malicious URLs Detection
verfasst von : Qisheng Chen, Kazumasa Omote
Erschienen in: ICT Systems Security and Privacy Protection
Verlag: Springer Nature Switzerland
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Abstract
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.