# Detection of Malicious Websites Using Symbolic Classifier
## 5 Conclusions  
In this paper, the GPSC algorithm was applied to publicly available datasets to investigate if, using the aforementioned method, the symbolic expression could be obtained that can detect malicious websites with high classification accuracy. To improve classification accuracy, the GPSC was combined with a random hyperparameter search and 5-fold cross-validation. Additionally, different dataset balancing methods were investigated to see if the classification accuracy could be improved. Based on the conducted investigations, the following conclusions can be drawn, i.e.,  
* The GPSC algorithm can be used to obtain symbolic expressions which could detect malicious websites with high classification accuracy;
* The application of GPSC with the random hyperparameter search method and 5-fold CV on various types of datasets achieved almost similar classification accuracy when compared to GPSC with the random hyperparameter search method;
* The application of oversampling methods showed that the samples of the minority class could be synthetically increased to balance the dataset classes and in the end improve classification accuracy. The KmeansSMOTE was the only dataset with which GPSC produced symbolic expression with high classification accuracy in both cases (with and without 5-fold CV). The advantages of the proposed method are:
* After training using GPSC, the symbolic expression (mathematical equation) is obtained;
* Dataset undersampling and oversampling methods can improve the classification accuracy of the obtained symbolic expressions;
* 5-fold cross-validation process with random hyper-parameter search proved to be a powerful tool in generating symbolic expressions with high classification accuracy in the detection of malicious websites. The disadvantages of proposed method are:
* The CPU time needed to train the GPSC algorithm depends on dataset size and GPSC hyperparameter values, i.e., larger datasets, population size, and number of generations could prolong the GPSC execution time;
* The parsimony coefficient is one of the most sensitive hyperparameters in GPSC. The range which will be used in the investigation had to be defined initially through trial and error. The low values can result in a bloat phenomenon while large values can choke the population and prevent the growth of each population member in size. Future work regarding the detection of malicious websites will be focused on enlarging the dataset, especially the number of original malicious websites in the dataset. Another part of the future work will be focused on the improvement of classification accuracies by way of the implementation of dataset scaling/normalizing methods. Conceptualization, N.A., S.B.S. and I.L. ; methodology, S.B.S. and M.G. ; software, N.A. and I.L. ; validation, N.A., IL. and M.G. ; formal analysis, N.A. ; investigation, N.A. and I.L. ; resources, S.B.S. and M.G. ; data curation, S.B.S. and M.G. ; writing--original draft preparation, N.A., S.B.S. and I.L. ; writing--review and editing, N.A., S.B.S. and I.L. ; visualization, N.A. and M.G. ; supervision, N.A. ; project administration, N.A. ; funding acquisition, N.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. This research received no external funding. This research received no external funding. This research received no external funding. This research received no external funding. This research received no external funding funding. This research received no external funding funding. This research received no external funding funding. This research received no external funding funding funding. This research received no external funding funding funding. This research received no external funding funding funding funding. This research received no external funding funding funding funding. This research received no external funding funding funding funding. Data Availability Statement: Publicly available dataset was used in this study.