This thesis discusses the development of an efficient intrusion detection system (IDS) specifically for denial of service attacks, utilizing a minimal subset of network traffic attributes to enhance detection performance. By employing various machine learning algorithms, the study identifies the best performing classifier and emphasizes the importance of selecting relevant features for improving classification accuracy. The proposed model shows significant improvements over traditional methods and aims to aid network administrators in implementing effective IDS solutions.