Internet firewalls are a composite of both hardware and software components, which are employed to enforce a security policy dictating the movement of data between many networks. Conventional firewalls depend on pre-established rules and signatures in order to identify and prevent the transmission of harmful network traffic. Nevertheless, it is worth noting that the aforementioned regulations and authentication methods frequently remain unchanging and can be effortlessly circumvented by highly skilled assailants. This analysis improves the use of firewall in detecting internet attacks using machine learning techniques. This study introduces a novel approach to enhance internet firewall efficacy through the integration of machine learning techniques. By leveraging a sophisticated model, the proposed system achieves exceptional performance, attaining a remarkable 99.99% precision, recall, and F1-score. This significant advancement in accuracy demonstrates the potential of employing machine learning in fortifying internet security infrastructure. The model's ability to consistently and reliably discern malicious activities from benign traffic showcases its robustness in real-world scenarios, thus presenting a promising avenue for bolstering network defense mechanisms. This research not only contributes to the burgeoning field of cybersecurity but also lays the foundation for future innovations in adaptive and intelligent firewall systems.
| Published in | American Journal of Computer Science and Technology (Volume 6, Issue 4) |
| DOI | 10.11648/j.ajcst.20230604.14 |
| Page(s) | 170-179 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Firewall, Machine Learning, Cyber-Attacks, Response Policy
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APA Style
Ozohu Musa, M., Victor-Ime, T. (2023). Improving Internet Firewall Using Machine Learning Techniques. American Journal of Computer Science and Technology, 6(4), 170-179. https://doi.org/10.11648/j.ajcst.20230604.14
ACS Style
Ozohu Musa, M.; Victor-Ime, T. Improving Internet Firewall Using Machine Learning Techniques. Am. J. Comput. Sci. Technol. 2023, 6(4), 170-179. doi: 10.11648/j.ajcst.20230604.14
AMA Style
Ozohu Musa M, Victor-Ime T. Improving Internet Firewall Using Machine Learning Techniques. Am J Comput Sci Technol. 2023;6(4):170-179. doi: 10.11648/j.ajcst.20230604.14
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Download@article{10.11648/j.ajcst.20230604.14,
author = {Martha Ozohu Musa and Temitope Victor-Ime},
title = {Improving Internet Firewall Using Machine Learning Techniques},
journal = {American Journal of Computer Science and Technology},
volume = {6},
number = {4},
pages = {170-179},
doi = {10.11648/j.ajcst.20230604.14},
url = {https://doi.org/10.11648/j.ajcst.20230604.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20230604.14},
abstract = {Internet firewalls are a composite of both hardware and software components, which are employed to enforce a security policy dictating the movement of data between many networks. Conventional firewalls depend on pre-established rules and signatures in order to identify and prevent the transmission of harmful network traffic. Nevertheless, it is worth noting that the aforementioned regulations and authentication methods frequently remain unchanging and can be effortlessly circumvented by highly skilled assailants. This analysis improves the use of firewall in detecting internet attacks using machine learning techniques. This study introduces a novel approach to enhance internet firewall efficacy through the integration of machine learning techniques. By leveraging a sophisticated model, the proposed system achieves exceptional performance, attaining a remarkable 99.99% precision, recall, and F1-score. This significant advancement in accuracy demonstrates the potential of employing machine learning in fortifying internet security infrastructure. The model's ability to consistently and reliably discern malicious activities from benign traffic showcases its robustness in real-world scenarios, thus presenting a promising avenue for bolstering network defense mechanisms. This research not only contributes to the burgeoning field of cybersecurity but also lays the foundation for future innovations in adaptive and intelligent firewall systems.
},
year = {2023}
}
TY - JOUR T1 - Improving Internet Firewall Using Machine Learning Techniques AU - Martha Ozohu Musa AU - Temitope Victor-Ime Y1 - 2023/11/29 PY - 2023 N1 - https://doi.org/10.11648/j.ajcst.20230604.14 DO - 10.11648/j.ajcst.20230604.14 T2 - American Journal of Computer Science and Technology JF - American Journal of Computer Science and Technology JO - American Journal of Computer Science and Technology SP - 170 EP - 179 PB - Science Publishing Group SN - 2640-012X UR - https://doi.org/10.11648/j.ajcst.20230604.14 AB - Internet firewalls are a composite of both hardware and software components, which are employed to enforce a security policy dictating the movement of data between many networks. Conventional firewalls depend on pre-established rules and signatures in order to identify and prevent the transmission of harmful network traffic. Nevertheless, it is worth noting that the aforementioned regulations and authentication methods frequently remain unchanging and can be effortlessly circumvented by highly skilled assailants. This analysis improves the use of firewall in detecting internet attacks using machine learning techniques. This study introduces a novel approach to enhance internet firewall efficacy through the integration of machine learning techniques. By leveraging a sophisticated model, the proposed system achieves exceptional performance, attaining a remarkable 99.99% precision, recall, and F1-score. This significant advancement in accuracy demonstrates the potential of employing machine learning in fortifying internet security infrastructure. The model's ability to consistently and reliably discern malicious activities from benign traffic showcases its robustness in real-world scenarios, thus presenting a promising avenue for bolstering network defense mechanisms. This research not only contributes to the burgeoning field of cybersecurity but also lays the foundation for future innovations in adaptive and intelligent firewall systems. VL - 6 IS - 4 ER -
Department of Cyber Security, University of Port Harcourt, Port Harcourt, Nigeria
