Language models have shown strong potential for intrusion detection, but their high memory and computational demands make them difficult to deploy in resource-constrained IoT environments. In this work, we propose a compact Transformer-based architecture that compresses the input into a small latent space before processing it with a minimal Transformer core. We analyze nine compression configurations, compare parameter sharing with layer stacking, and evaluate our approach against SecurityBERT on two IoT intrusion detection datasets, Edge-IIoTset and CICIoT2023. With only 306k non-embedding parameters and 1.6M parameters in total (6MB), our model is about 7x smaller than SecurityBERT (11.2M parameters, 45MB). With the same privacy-preserving encoding, it outperforms SecurityBERT by about 2 points of macro F1-score on Edge-IIoTset and is within 0.7 points on CICIoT2023. It is also about 89x faster on a simulated edge device. We further show that a single Transformer layer is sufficient, and that parameter sharing matches layer stacking with up to 66% fewer parameters. These findings show that lightweight Transformer-based architectures can provide a practical and accurate solution for intrusion detection in resource-constrained IoT settings

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