Abstract

With the advancement of wireless communication, numerous security threats have emerged on the internet. Intrusion Detection Systems (IDS) play a crucial role in identifying attacks and detecting intruders. Previously, various machine learning (ML) techniques have been applied to IDS in efforts to enhance intrusion detection and improve accuracy. This paper proposes a novel approach to developing an efficient IDS by utilizing Principal Component Analysis (PCA) and the Random Forest classification algorithm. PCA is employed to reduce the dataset's dimensionality, while Random Forest assists in accurate classification. The results demonstrate that this approach outperforms other techniques, such as SVM, Naïve Bayes, and Decision Tree, in terms of accuracy. The proposed method achieves a performance time of 3.24 minutes, an accuracy rate of 96.78%, and an error rate of 0.21%.