Cross-domain intrusion detection, a critical component of cybersecurity, involves evaluating the performance of neural networks across diverse datasets or databases. The ability of intrusion detection systems to effectively adapt to new threats and data sources is paramount for safeguarding networks and sensitive information. This research delves into the intricate world of cross-domain intrusion detection, where neural networks must demonstrate their versatility and adaptability. The results of our experiments expose a significant challenge: the phenomenon known as catastrophic forgetting. This is the tendency of neural networks to forget previously acquired knowledge when exposed to new information. In the context of intrusion detection, it means that as models are sequentially trained on different intrusion detection datasets, their performance on earlier datasets degrades drastically. This degradation poses a substantial threat to the reliability of intrusion detection systems. In response to this challenge, this research investigates potential solutions to mitigate the effects of catastrophic forgetting. We propose the application of continual learning techniques as a means to address this problem. Specifically, we explore the Elastic Weight Consolidation (EWC) algorithm as an example of preserving previously learned knowledge while allowing the model to adapt to new intrusion detection tasks. By examining the performance of neural networks on various intrusion detection datasets, we aim to shed light on the practical implications of catastrophic forgetting and the potential benefits of adopting EWC as a memory-preserving technique. This research underscores the importance of addressing catastrophic forgetting in cross-domain intrusion detection systems. It provides a stepping stone for future endeavours in enhancing multi-task learning and adaptability within the critical domain of intrusion detection, ultimately contributing to the ongoing efforts to fortify cybersecurity defences.