Combating Fake Cyber Threat Intelligence using Provenance in Cybersecurity Knowledge Graphs

被引：7

作者：

Mitra, Shaswata ^{[1
]}

Piplai, Aritran ^{[2
]}

Mittal, Sudip ^{[1
]}

Joshi, Anupam ^{[2
]}

机构：

[1] Mississippi State Univ, Dept Comp Sci & Engn, Mississippi State, MS 39762 USA

[2] Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Baltimore, MD 21228 USA

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2021年

基金：

美国国家科学基金会;

关键词：

Cybersecurity; Fake Data; Provenance; Cyber Threat Intelligence;

D O I：

10.1109/BigData52589.2021.9671867

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Today there is a significant amount of fake cybersecurity related intelligence on the internet. To filter out such information, we build a system to capture the provenance information and represent it along with the captured Cyber Threat Intelligence (CTI). In the cybersecurity domain, such CTI is stored in Cybersecurity Knowledge Graphs (CKG). We enhance the exiting CKG model to incorporate intelligence provenance and fuse provenance graphs with CKG. This process includes modifying traditional approaches to entity and relation extraction. CTI data is considered vital in securing our cyberspace. Knowledge graphs containing CTI information along with its provenance can provide expertise to dependent Artificial Intelligence (AI) systems and human analysts.

引用

页码：3316 / 3323

页数：8

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