Enhancing web traffic attacks identification through ensemble methods and feature selection
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866916538365247488 |
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| author | Urda, Daniel Martínez, Branly Basurto, Nuño Kull, Meelis Arroyo, Ángel Herrero, Álvaro |
| author_facet | Urda, Daniel Martínez, Branly Basurto, Nuño Kull, Meelis Arroyo, Ángel Herrero, Álvaro |
| contents | Websites, as essential digital assets, are highly vulnerable to cyberattacks because of their high traffic volume and the significant impact of breaches. This study aims to enhance the identification of web traffic attacks by leveraging machine learning techniques. A methodology was proposed to extract relevant features from HTTP traces using the CSIC2010 v2 dataset, which simulates e-commerce web traffic. Ensemble methods, such as Random Forest and Extreme Gradient Boosting, were employed and compared against baseline classifiers, including k-nearest Neighbor, LASSO, and Support Vector Machines. The results demonstrate that the ensemble methods outperform baseline classifiers by approximately 20% in predictive accuracy, achieving an Area Under the ROC Curve (AUC) of 0.989. Feature selection methods such as Information Gain, LASSO, and Random Forest further enhance the robustness of these models. This study highlights the efficacy of ensemble models in improving attack detection while minimizing performance variability, offering a practical framework for securing web traffic in diverse application contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16791 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing web traffic attacks identification through ensemble methods and feature selection Urda, Daniel Martínez, Branly Basurto, Nuño Kull, Meelis Arroyo, Ángel Herrero, Álvaro Cryptography and Security Artificial Intelligence Machine Learning Websites, as essential digital assets, are highly vulnerable to cyberattacks because of their high traffic volume and the significant impact of breaches. This study aims to enhance the identification of web traffic attacks by leveraging machine learning techniques. A methodology was proposed to extract relevant features from HTTP traces using the CSIC2010 v2 dataset, which simulates e-commerce web traffic. Ensemble methods, such as Random Forest and Extreme Gradient Boosting, were employed and compared against baseline classifiers, including k-nearest Neighbor, LASSO, and Support Vector Machines. The results demonstrate that the ensemble methods outperform baseline classifiers by approximately 20% in predictive accuracy, achieving an Area Under the ROC Curve (AUC) of 0.989. Feature selection methods such as Information Gain, LASSO, and Random Forest further enhance the robustness of these models. This study highlights the efficacy of ensemble models in improving attack detection while minimizing performance variability, offering a practical framework for securing web traffic in diverse application contexts. |
| title | Enhancing web traffic attacks identification through ensemble methods and feature selection |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.16791 |