Automatic Identification of Indicators of Compromise using Neural-Based Sequence Labelling

TL;DR

This paper introduces a neural sequence labelling approach with attention mechanisms to automatically identify Indicators of Compromise in cybersecurity reports, eliminating the need for expert-crafted features.

Contribution

It is the first to apply end-to-end neural sequence labelling with attention to IOCs detection, improving accuracy over existing models without requiring cybersecurity expertise.

Findings

Achieves over 88% average F1-score in IOCs identification

Outperforms other sequence labelling models

Effectively detects low frequency IOCs in long sentences

Abstract

Indicators of Compromise (IOCs) are artifacts observed on a network or in an operating system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an early stage. Thus, they exert an important role in the field of cybersecurity. However, state-of-the-art IOCs detection systems rely heavily on hand-crafted features with expert knowledge of cybersecurity, and require a large amount of supervised training corpora to train an IOC classifier. In this paper, we propose using a neural-based sequence labelling model to identify IOCs automatically from reports on cybersecurity without expert knowledge of cybersecurity. Our work is the first to apply an end-to-end sequence labelling to the task in IOCs identification. By using an attention mechanism and several token spelling features, we find that the proposed model is capable of identifying the low frequency IOCs from long sentences contained in cybersecurity reports. Experiments show that the proposed model outperforms other sequence labelling models, achieving over 88% average F1-score.