# Understanding the Influence of AST-JS for Improving Malicious Webpage Detection
## 3 Overview of Malicious Webpage Detection
### Ast-Js
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cluster** & **Label** & **Percentage (\%)** \\ \hline
0 & Benign & 57 \\  & Malicious & 58 \\ \hline
1 & Benign & 42 \\  & Malicious & 64 \\ \hline
2 & Benign & 78 \\  & Malicious & 78 \\ \hline
3 & Benign & 17 \\  & Malicious & 67 \\ \hline
4 & Benign & 50 \\  & Malicious & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Percentage of unique hostnames per URL in each cluster. focus on phishing attacks, which do not need complicated payloads. Moreover, this study did not include real-time application analysis as a plug-in in web browsers to check the actual performance in real conditions. In addition, due to the use of a machine learning model, our proposed approach is susceptible to an adversarial attack that can affect network parameters, making our model produce many false negatives. Conceptualization, M.F.R., S.O. and T.B. ; methodology, M.F.R., S.K., S.O., T.B. and T.T. ; software, M.F.R. ; validation, S.K., S.O., T.B., T.T. and D.I. ; formal analysis, M.F.R. ; investigation, M.F.R. ; resources, S.O. and D.I. ; data curation, M.F.R. ; writing--original draft preparation, M.F.R. ; writing--review and editing, M.F.R., S.O., T.B., T.T. and D.I. ; visualization, M.F.R. ; supervision, S.K., S.O., T.B., T.T. and D.I. ; funding acquisition, D.I. All authors have read and agreed to the published version of the manuscript. This work was partially supported by JSPS/MEXT KAKENHI Grant Numbers JP21H0344 and JP21 KK0178. Not applicable. The authors declare no conflict of interest. ## References

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