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Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP …