计算机科学
妥协
注释
构造(python库)
棱锥(几何)
计算机安全
互联网
命名实体识别
人工智能
数据挖掘
机器学习
数据科学
万维网
任务(项目管理)
工程类
社会科学
社会学
物理
系统工程
光学
程序设计语言
作者
Hsin-Ju Chan,Chin-Yuan Hsu,Ching-Chang Chien,Ji-Jie Wu,He-Lin Ku
标识
DOI:10.1109/bigdata55660.2022.10020985
摘要
With the increasing use of the internet, cyber threats and malicious activities are becoming ubiquitous. To avoid unsuspecting attacks, gathering enough information about different threats is crucial. According to the Pyramid of Pain, Indicators of Compromise (IOCs) are the simplest artifacts to observe, which help cyber security professionals to design the corresponding precautions. Cyber Threat Intelligence (CTI) is data that presents current threat events, threat actors’ targets, and attack behaviors; hence, collecting and analyzing CTI in advance can be beneficial to defend against cyberattacks. In this paper, we construct a named entity recognition dataset using our annotation method by collecting 1,854 threat intelligence reports. Additionally, we fine-tuned four pre-trained language models and compared the efficiency of each model. Among the four models, we realized that the fine-tuned ELECTRA model could extract new IOCs correctly, and the FeedRef2022 dataset could train NER models for detecting IOCs.
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