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Joint extraction from unstructured text aims to extract relational triples composed of entity pairs and their relations. However, most existing works fail to process the overlapping issues that occur when the same entities are utilized to generate different relational triples in a sentence. In this work, we propose a mutually exclusive Binary Cross Tagging (BCT) scheme and develop the end-to-end BCT framework to jointly extract overlapping entities and triples. Each token of entities is assigned a mutually exclusive binary tag, and then these tags are cross-matched in all tag sequences to form triples. Our method is compared with other state-of-the-art models in two English public datasets and a large-scale Chinese dataset. Experiments show that our proposed framework achieves encouraging performance in F1 scores for the three datasets investigated. Further detailed analysis demonstrates that our method achieves strong performance overall with three overlapping patterns, especially when the overlapping problem becomes complex.

Recognizing attack intention is crucial for security analysis. In recent years, a number of methods for attack intention recognition have been proposed. However, most of these techniques mainly focus on the alerts of an intrusion detection system and use algorithms of low efficiency that mine frequent attack patterns without reconstructing attack paths. In this paper, a novel and effective method is proposed, which integrates several techniques to identify attack intentions. Using this method, a Bayesian-based attack scenario is constructed, where frequent attack patterns are identified using an efficient data-mining algorithm based on frequent patterns. Subsequently, attack paths are rebuilt by recorrelating frequent attack patterns mined in the scenario. The experimental results demonstrate the capability of our method in rebuilding attack paths, recognizing attack intentions as well as in saving system resources. Specifically, to the best of our knowledge, the proposed method is the first to correlate complementary intrusion evidence with frequent pattern mining techniques based on the FP-Growth algorithm to rebuild attack paths and to recognize attack intentions.