Security is getting substantial focus in many industries, especially safety-critical ones. When new regulations and standards which can run to hundreds of pages are introduced, it is necessary to identify the requirements in those documents which have an impact on security. Additionally, it is necessary to revisit the requirements of existing systems and identify the security-related ones. We investigate the feasibility of using a classifier for security-related requirements trained on requirement specifications available online. We base our investigation on 15 requirement documents, randomly selected and partially pre-labeled, with a total of 3,880 requirements. To validate the model, we run a cross-project prediction on the data where each specification constitutes a group. We also test the model on three different UN regulations from the automotive domain with different magnitudes of security relevance. Our results indicate the feasibility of training a model from a heterogeneous data set including specifications from multiple domains and in different styles. Additionally, we show the ability of such a classifier to identify security requirements in real-life regulations and discuss scenarios in which such classification becomes useful to practitioners.