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Anomaly-based detection is a novel form of an intrusion detection system, which has become the focus of many researchers for cybersecurity systems. Data manages most business decisions. With more access to data, it is necessary to interrupt and analyze them correctly. When it comes to security, the first step is to determine the outliers as a security threat. Machine learning and deep learning techniques have proven to recognize anomalous attack patterns that deviate from normal network behavior. Machine learning can be utilized to learn the characteristic of data and help to improve the speed of detection. In this research, we present our approach to implementing an algorithm for the anomaly detection framework in complex and unbalanced data. The proposed method has been applied to a CSE-CIC-IDS2018 dataset. It is the most recent dataset that is publicly available, an extensive dataset that includes a …