Abstract: Occasional occurrences of random destructive acts targeting individual computers or entire networks are observed on the internet. With the exponential growth of computer connections, keeping pace with such threats becomes increasingly challenging. Security concerns in cyberspace mirror those in physical environments. The Intrusion Detection System (IDS) serves as a critical tool for identifying and analyzing such host00ile behaviors across networks, aiding in attack detection and intruder identification. To enhance IDS performance, various machine learning (ML) techniques have been integrated into intrusion detection systems. This study proposes an efficient IDS strategy leveraging Principal Component Analysis (PCA) and the Convolutional Neural Network (CNN) classification algorithm. PCA organizes data by reducing its dimensionality, while CNN efficiently categorizes data using random forest. Tests conducted on the KDD Knowledge Discovery Dataset demonstrate the superiority of the proposed method over alternative approaches such as Support Vector Machines (SVM), Naive Bayes, and Decision Trees in terms of accuracy. Results from our proposed method reveal a performance time of 2.41 minutes, an accuracy rate of 98.63%, and a mistake rate of only 0.18%. This approach showcases promising potential for enhancing IDS effectiveness, offering robust defense mechanisms against cyber threats in network environments.