Implementing effective control over harmful actions in a network through an Intelligent Detection System (IDS) is necessary for modern digital security, but building robust techniques for coverage with high accuracy remains a challenge. To help overcome this challenge, the study’s contribution proposes a hybrid deep learning approach combining convolution neural networks (CNN) and Long Short Term Memory (LSTM) networks for maximum coverage of detections in IDS. Tests have been conducted on various datasets and the model achieved best results of 75% detection accuracy for different attack scenarios. This was better than what’s achieved using traditional methods as the legacy Intelligent Detection Systems (IDS) techniques, although increasing detection coverage, reduced the level of falsely identified cases and improved adaptability towards new patterns of attacks. The results open new frontiers for the development of hybrid machine learning architectures capable of addressing the shortcomings of traditional Intelligent Detection Systems (IDS) models and improving network security.