There is a growing need for intelligent adaptive approaches because traditional Intrusion Detection Systems (IDS) are insufficient in the face of new cybersecurity threats. As the artificial intelligence technology becomes ever so powerful, deep learning-based techniques provide excellent ways to increase the power of intrinsic IDSs. Custom deep learning models are then built and trained to enhance the metrics of intrusion detection, specifically DNNs and CNNs. Whereas traditional methods directly use libraries to deep learning algorithms, we approach model construction from the base up for dataset specific fine-tuning. It also allows for better detection accuracy and process efficiency. The experimental results validate that our models perform comparably with popular IDS solutions and provide the advantages of a customized deep learning solution. We further emphasize the benefit of bespoke models in cybersecurity by using deep learning architectures that have been specifically tailored for intrusion detection. The positive impact of our specialized approach on threat detection highlights the necessity for model customization to improve IDS performance.