Abstract
Cyber-attack is a very hot topic today. Nowadays, systems must always be connected to the internet, and network infrastructure keeps growing in both scale and complexity. Therefore, the problem of detecting and warning cyber-attacks is now very urgent. To improve the effectiveness of detecting cyber-attacks, many methods and techniques were applied. In this paper, we propose to apply two methods of optimizing cyber-attack detection based on the IDS 2018 dataset using Principal Component Analysis (PCA) and machine learning algorithms. In the experimental section, we compare and evaluate the efficiency of the algorithm through 2 parameters: detection and processing time, and the accuracy of the algorithm. The experimental results show that the model using optimized features has brought an apparent and better effect than models that have not reduced the feature dimension. Keywords— PCA; Network traffic; Anomaly; Cyberattack detection.
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