In recent years, the era of large-scale models has dawned upon our daily lives, with the majority of smartphone vendors integrating these sophisticated technologies into their devices’ systems. Smartphones have become an inseparable part of our existence, housing private assets, information, and intimate details of our lives. Consequently, the threat of attacks targeting personal mobile devices has escalated, rendering cybersecurity a paramount concern of our times. To address this pressing issue, this paper presents a deep neural network, leveraging the ubiquitous Transformer architecture, specifically tailored to identify network intrusions. The proposed model, through its intricate design and powerful capabilities, has achieved a remarkable accuracy of 98.54% when evaluated on the esteemed NF-UQ-NIDS-V2 dataset. This achievement underscores the potential of Transformer-based solutions in fortifying the cybersecurity landscape, safeguarding users’ data and privacy against the ever-evolving landscape of cyber threats.