Detecting fishing URLs using fuzzy clustering algorithms with global optimization
DOI:
https://doi.org/10.34185/1562-9945-2-139-2022-06Keywords:
confidential data, classifier, fuzzy clustering of C-means, phishing attacks, global optimization, subtractive clusteringAbstract
An algorithm for detecting phishing URLs (classifier) using fuzzy clustering is proposed, which includes choosing the type of intelligent classifier and justifying its parameters using global optimization methods. The following were studied as intellectual classifiers: subtractive clustering and fuzzy clustering of C-means. To find (adjust) the optimal (for a specific task) parameters of intelligent classifiers, the use of global optimization methods is justified, including genetic algorithm, direct random search, annealing simulation method, multicriteria optimization and threshold acceptance method. As a criterion of global optimization, a combined criterion was used, which includes the definition of the regularity criterion calculated on the test sample and the bias (minimum shift) criterion based on the analysis of solutions. By modeling in the Matlab environment with the help of standard and developed programs, the evaluated efficiency of using the proposed algorithm is evaluated on the example of experimental data – a set of 150 phishing and 150 secure URLs. The set of experimental data included information about the domain name registrar, the lifetime of the domain, the geolocation of the hosting server, the presence of a secure connection with a valid certificate. By simulation it is established that the fuzzy classifier with the subtractive clustering algorithm and using the Sugeno structure and 6 clusters meets the minimum of the combined criterion. All phishing URLs that were mistakenly classified as secure were found to have a secure con-nection with a valid certificate. Thus, further research should be aimed at exploring additional informative attributes (features) that could allow better separation of phishing and secure URLs.
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