SNI-CIDS: Collaborative intrusion detection using modified ensemble stacking deep neural networks for new network integration in heterogeneous networks

In today’s interconnected world, protecting heterogeneous networks from emerging cybersecurity threats is increasingly critical, particularly during the integration of new networks, which often introduce unknown security postures and inconsistent measures. These uncertainties expand the attack surface and create vulnerabilities that attackers can exploit during the transition phase. To address these challenges, this research proposes a Collaborative Intrusion Detection System (CIDS) named Stacked Network Integration CIDS (SNI-CIDS), which Modified Ensemble Stacking Deep Neural Networks (MES-DNN) to enhance intrusion detection in heterogeneous networks. The model is built using diverse public Intrusion Detection System (IDS) datasets to represent heterogeneous environments, and the meta-model is fine-tuned with traffic samples from newly integrated networks for adaptability. Experimental results demonstrate that SNI-CIDS achieves robust detection performance with an average accuracy of 96.67%, recall of 95.82%, precision of 95.17%, and F1-Score of 95.36% when analyzing overall traffic. In the specific scenario of new network integration, SNI-CIDS maintains strong results with an accuracy of 89.23%, recall of 86.55%, precision of 71.52%, and F1-Score of 71.09%, showcasing its efficacy in securing both new and existing networks in heterogeneous environments.