In a digital world, cyber-attacks are increasingly common, raising concerns that existing anomaly detection models might not effectively handle intricate threat scenarios. As the demands for network systems grow. Historically, the update and reset gates of the Gated Recurrent Unit (GRU) designed to controls flow in input data across time steps have faced challenges in identifying anomalies in security monitoring, traffic log analyses, and packet flow assessments. To address anomalies, reduce time expenditure, and improve network security, the proposed research utilizes a Deep Learning (DL) technique named Structured Activation Module Loop Framework Unit and an efficient activation module unit, which integrates a Bidirectional Gated Recurrent Unit (Bi-GRU) model that includes update and reset gates for controlling information flow on the basis of the classification context of the input data. The suggested structured activated loop framework monitors error data straight in the update gate without demanding the reset gate, enabling several checks in a loop format. The activation module unit precisely divides the class data to predict the appropriate output characteristics for resolving missing values. This utilizes network intrusion datasets (UNSW-NB15) and Neural Simulation Language- Knowledge Discovery in Databases (NSL-KDD), both commonly used for (NID) Network Intrusion Detection systems, along with pre-processing data and assessments for data splitting through training the model and testing procedures. Similarly, the proposed performance is assessed using different metrics such as F1-meaures, recall, value of precision, accuracy, with overall accuracy to evaluate the efficiency of the suggested deep learning study. The results obtained precisions of 0.99, 0.97, 0.97, and 0.97 in NSL-KDD, 0.98, 0.99, 0.98, and 0.98 in UNSW-NB 15. Still, the assessment of recommended models demonstrates the efficacy of the research. The present research attempts for find and improve creation of detection of anomaly models for network and cyber security regarding hackers and malicious attacks.