The volume of published scientific literature available on Internet has been increasing exponentially. Some of them reflect the latest achievement of the specific research domain. In recent years, many projects have been funded aiming to online scientific literature mining, especially in biomedical research. Scientific literature covers most of the hot topics in the research field and has a very large domain-specific vocabulary. The exploitation of domain knowledge and specialized vocabulary can dramatically improve the result of literature text processing. The purpose of this paper is to identify the frequently used keywords in IT security literatures. The result then can be utilized to improve the performance of automatic IT security document retrieval, identification and classification. Our method is to query CiteseeX to retrieve source data of paper description information and build an artificially annotated corpus. Over the corpus, we perform words frequency statistics, word co-occurrence analysis, discrimination index computation, retrieval efficiency analysis and thus build a lexicon of IT security based on our experimental result. The lexicon can further be used in improving retrieval performance and assisting new words discovering and document classification.