Supervised detection models can be deployed in computer security detection systems to strengthen detection. However, acquiring a training dataset is particularly expensive in this context since expert knowledge is required to annotate. Some research works rely on active learning to reduce human effort, but they often assimilate annotators to mere oracles providing ground-truth labels. Most of them completely overlook the user experience while active learning is an interactive procedure. In this paper, we introduce an end-to-end active learning system, ILAB, tailored to computer security experts needs. We have designed the active learning strategy and the user interface jointly to effectively reduce annotation effort. Our user experiments show that ILAB is an efficient active learning system that computer security experts can deploy in real-world annotation projects.