The web security field constantly changes, with new threats and vulnerabilities continually developing. Due to this, there is constant space for development and innovation in this industry. There are various future development prospects that could improve the efficacy of web security solutions in this field. Firstly, to improve data quality and accuracy, data cleaning processes are one area that still needs improvement (Foot, 2022). Data accuracy, consistency, and redundancy are all addressed at the crucial data cleaning stage of the data preprocessing process. A more accurate representation of the features of the websites being studied in the data utilized by online security systems could be achieved by improving this procedure.
The investigation of various feature selection techniques represents another area for future development. In feature selection, the most pertinent features are chosen to be included in the classification model. Numerous methods exist for selecting features, including wrapper-based, mutual information-based, and correlation-based methods (Sharmin et al., 2019). Researchers can determine the most efficient method for accurately classifying websites by experimenting with various feature selection strategies. Additionally, testing with different algorithms and their default configurations could improve the efficacy of web security solutions. Several machine learning algorithms can categorize websites, including decision trees, random forests, support vector machines, and neural networks. The model’s performance can, however, be significantly impacted by the method and its chosen hyperparameters. Researchers can find the best method for website classification by experimenting with several algorithms and their default settings.
Some variables can be interpolated, and NA values can be eliminated to increase accuracy and malicious recall. Interpolation is substituting approximated values based on pre-existing data for missing values (Awati, 2022). Researchers can increase the classification model’s precision and the system’s capacity to identify dangerous websites by interpolating some variables and eliminating NA values.
In order to increase our project in the future, there are a few things that we could implement. For example, implementing the CatBoost gradient boosting framework may improve classification precision. The addition of the CatBoost algorithm in future research could increase the precision of web security systems because it has demonstrated potential in classifying websites as safe or risky. Additionally, the accuracy of the categorization model could be increased by expanding the feature selection process to consider new characteristics, such as those generated from website content or data on user activity. Including these attributes might improve the system’s capacity to identify dangerous websites and result in a more thorough grasp of website characteristics. Additionally, sampling from other sources, such as specific economic sectors or geographic regions, could enhance the dataset development process and result in a more diverse and representative dataset for categorization (Moss, 2020). By diversifying the information, researchers can build a more reliable system that can accurately categorize websites from different industries and geographical areas.
In conclusion, there are many different ways that web security research can grow in the future. By experimenting with various feature selection techniques, researchers can increase the accuracy and dependability of classification models.
Conclusion
According to the findings, KNN performed better than SVM and RF regarding overall score, although RF had higher consistency and good precision. However, a fundamental issue that could lead to bias in models trained on imbalanced datasets is the need for more controls to counteract dataset imbalance. Future research should employ controls to address dataset imbalance and take a more comprehensive approach by utilizing numerous algorithms and multiple datasets to identify each one’s advantages and disadvantages in order to get beyond this constraint. Before beginning any new work, there is also a need for a more organized and thorough method of assessing prior analysis.
References
Awati. (2022, May 25). What are extrapolation and interpolation? WhatIs.com. https://www.techtarget.com/whatis/definition/extrapolation-and-interpolation
Foot. (2022). 8 proactive steps to improve data quality. TechTarget. https://www.techtarget.com/searchdatamanagement/feature/Proactive-practices-for-data-quality-improvement
Moss. (2020, August 10). What is the purpose of sampling in research? CloudResearch. https://www.cloudresearch.com/resources/guides/sampling/what-is-the-purpose-of-sampling-in-research/
Sharmin, S., Shoyaib, M., Ali, A. A., Khan, M. A., & Chae, O. (2019). Simultaneous feature selection and discretization based on mutual information. Pattern Recognition, 91, 162-174. https://doi.org/10.1016/j.patcog.2019.02.016
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