Anomalies in network traffic pose a significant challenge in contemporary cybersecurity. Traditional intrusion detection systems (IDS) often struggle to effectively identify subtle and evolving anomalies indicative of sophisticated attacks. This paper introduces HID-AG, an IDS designed to address prominent challenges in cybersecurity, particularly anomalies. HID-AG, through the integration of HybridIDNet, leverages the combined strength of CNNs, RNNs, and RF to tackle these challenges head-on. The CNN component of HybridIDNet excels in spatial feature extraction, allowing HID-AG to detect anomalies that manifest as intricate patterns in network traffic. RNNs, on the other hand, specialize in capturing temporal dependencies, enabling the system to discern anomalies rooted in the sequential behaviours of cyber threats. The incorporation of Random Forests (RF) adds a layer of interpretability, enhancing the system’s robustness in identifying anomalies across diverse data types. The proposed method, based on HybridIDNet with CNNs, RNNs, and RF, demonstrates an impressive accuracy of 97.84%, signifying its efficacy in handling the complex nature of cybersecurity threats. The Python implementation of HID-AG ensures accessibility and transparency, providing cybersecurity professionals with a practical tool to combat the challenges associated with anomalies in real-world networks. This paper not only delves into the technical details of HID-AG but also presents a comprehensive analysis of its performance in the context of anomaly detection. By addressing challenges posed by anomalies, HID-AG emerges as a proactive cybersecurity solution, contributing to the ongoing efforts to fortify digital infrastructures against sophisticated and nuanced cyber threats. The open-source nature of the Python implementation encourages collaboration and further innovation in the realm of cybersecurity.