Volume 16 , Issue 1 , PP: 15-24, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Anggit Ferdita Nugraha 1 * , Yoga Pristyanto 2 , Beti Wulansari 3 , Dian Prasetya 4
Doi: https://doi.org/10.54216/JCIM.160102
A firewall is one of the devices that supports network security, especially at the organizational level. A Firewall's effectiveness in supporting network security is highly dependent on the capabilities and abilities of the Network Administrator. Unfortunately, the high complexity of creating rules and the process of configuring Firewall rules carried out statically by the Network Administrator weakens the effectiveness of the Firewall, and it cannot adapt to increasingly dynamic network pattern changes. Machine Learning is one of the potentials that can be used so that the Firewall can work adaptively. Adaptive Firewall configuration in recognizing various attacks in the network will undoubtedly increase the effectiveness of the Firewall in ensuring network security. The success of the machine learning model performance cannot be separated from the dataset used during the learning process. The dataset used in learning often has a large dimension, but various noises and attributes are irrelevant in representing one class of data. Therefore, it is necessary to support the feature selection technique, which will show the presence of relevant characteristics in the dataset and maximize the machine learning model's performance. This study will be conducted on adding feature selection techniques to develop machine learning models on the Benchmark dataset related to network security. Various popular feature selection techniques will be evaluated, and their performance will be compared based on scenarios between feature selection techniques or scenarios that only use a single classification.
Network Security , Firewall , Machine Learning , Feature Selection , Classification
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