Abstract
In the dynamic field of network security, the continuous emergence of
cyber threats demands advanced technologies that provide adaptive and
scalable defense mechanisms. This paper introduces a novel approach to
Intrusion Detection Systems (IDS) using Transformer models. Traditional
IDS methods struggle with scalability and adapting to new threats,
whereas our Transformer-based architecture leverages the model’s
scalability and parallel processing capabilities for real-time analysis
of large volumes of network traffic. Additionally, the adaptability and
generalization abilities of our model enable it to identify new and
sophisticated threats, thus enhancing the robustness of network
infrastructures against advanced cyber attacks. This study addresses a
critical gap in IDS research by demonstrating the efficacy of
Transformer models in this domain. Through rigorous empirical
evaluations on diverse datasets, we showcase the superior performance
and effectiveness of our proposed IDS, underscoring the transformative
potential of this technology in network security. Our findings not only
contribute to the growing body of literature on Transformer-based IDS
but also highlight the need for ongoing research and innovation in this
crucial field.