Zayid, E.I.M., Isah, I., Adam, Y.A., Farah, N.A.A., & Alshehri, O.A. (2023). Examine Website Defacement Dataset by Exploiting Some Classifiers’ Capabilities. Preprints. https://doi.org/10.20944/preprints202310.1743.v1
Chicago/Turabian Style
Zayid, E.I.M., Nadir Abdelrahman Ahmed Farah and Omar Abdullah Alshehri. 2023 "Examine Website Defacement Dataset by Exploiting Some Classifiers’ Capabilities" Preprints. https://doi.org/10.20944/preprints202310.1743.v1
Abstract
Website defacement is an illegal electronic act of changing a website. In this paper, robust machine learning classifiers’ capabilities were exploited to select the best input feature set for evaluating a website’s defacement risk. Zone-H, a private organization, offered us a defacement mining dataset. A sample of 93644 datapoints was concisely pre-processed and used for modelling purposes. Considering multidimensional features as input, reason and hackmode variables were chosen as outputs. Massive machine learning models were examined; however, decision tree (DT), k-nearest neighbour (k-NN), and random forest (RF) were the most powerful algorithms used to predict the target model. The input variables 'domain', 'system', 'web_server','redefacement', 'type', 'def_grade', and 'reason/hackmode' were tested and used to shape the final model. Using the cross-validation (CV) technique, the model’s key performance factors were calculated and reported. After calculating the average scores for the hyperbolic metrics (i.e., max-depth, min-sample-leaf, weight, max-features, and CV), both targets were evaluated, and the learning algorithms were ranked as RF, DT, and k-NN. The reason and hackmode variables were thoroughly analysed. The average score accuracies for the reason and hackmode targets were 0.85 and 0.585, respectively. The results showed a significant development in terms of modelling and optimizing website defacement risk. The study also successfully addresses the main cybersecurity concerns, in particular website defacement.
Keywords
prediction capabilities for website defacement; website defacement assessment; classification metrics for website hackticism; website defacement risks
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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