Network Intrusion Detection Systems (NIDS) play a crucial role in safeguarding network infrastructure against cyberattacks. As the prevalence and sophistication of these attacks increase, machine learning and deep neural network approaches have emerged as effective tools for enhancing NIDS capabilities in detecting malicious activities. However, the effectiveness of traditional deep neural models is often limited by the need for extensive labelled datasets and the challenges posed by data and feature heterogeneity across different network domains. To address these limitations, we developed a deep neural model that integrates multi-modal learning with domain adaptation techniques for classification. Our model processes data from diverse sources in a sequential cyclic manner, allowing it to learn from multiple datasets and adapt to varying feature spaces. Experimental results demonstrate that our proposed model significantly outperforms baseline neural models in classifying network intrusions, particularly under conditions of varying sample availability and probability distributions. The model's performance highlights its ability to generalize across heterogeneous datasets, making it an efficient solution for real-world network intrusion detection.