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 wide range of attack types. This data has been pre-processed and cleaned to find helpful information for classification by the proposed models. We performed a correlation methodology to filter irrelevant anomalies and grouped the correlated anomalies into a single feature to minimize detection time. A stacked autoencoder has been used to reduce the dimensionality of the dataset. We exploited different machine learning algorithms such as (Random Forest, GaussianNB, and multilayer perceptron) to classify the streamed data. Our experimental results outperformed the superiority of the proposed approach to identify anomalous components and manage threat detection in cybersecurity applications.