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Indicators of Compromise (IOCs) are artifacts observed on a network or in anoperating system that can be utilized to indicate a computer intrusion anddetect cyber-attacks in an early stage. Thus, they exert an important role inthe field of cybersecurity. However, state-of-the-art IOCs detection systemsrely heavily on hand-crafted features with expert knowledge of cybersecurity,and require large-scale manually annotated corpora to train an IOC classifier.In this paper, we propose using an end-to-end neural-based sequence labellingmodel to identify IOCs automatically from cybersecurity articles without expertknowledge of cybersecurity. By using a multi-head self-attention module andcontextual features, we find that the proposed model is capable of gatheringcontextual information from texts of cybersecurity articles and performs betterin the task of IOC identification. Experiments show that the proposed modeloutperforms other sequence labelling models, achieving the average F1-score of89.0% on English cybersecurity article test set, and approximately the averageF1-score of 81.8% on Chinese test set.

Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling