The study proposes a hybrid intrusion detection model combining k-means clustering and random forest classification to improve accuracy and reduce false alarm rates. Experiments using the NSL-KDD dataset achieved detection accuracy of 99.98% with a false alarm rate of 0.14%. The proposed approach outperforms existing models in both detection accuracy and alarm rate.