Enhanced Crime and Threat Intelligence Hunter with Named Entity Recognition and Sentiment Analysis

被引：0

作者：

Ng, James H. ^{[1
]}

Loh, Peter K. K. ^{[1
]}

机构：

[1] Singapore Inst Technol, Singapore, Singapore

来源：

SOFT COMPUTING FOR SECURITY APPLICATIONS, ICSCS 2022 | 2023年 / 1428卷

基金：

新加坡国家研究基金会;

关键词：

Cybercrime; Deep web crawler; Machine learning; Threat intelligence;

D O I：

10.1007/978-981-19-3590-9_23

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Collecting cybercrime evidence on the Internet typically involves reconnaissance and analyses of information extracted. Scouring the Internet, especially the Deep Web, often requires manual effort and is time-consuming. Hence, it is imperative to have an efficient framework for an intelligent tool to gather cyber intel automatically according to programmed directives. In this paper, we present an updated design to our prior threat intelligence hunter. We make use of machine learning for parsing linguistic intel.

引用

下载

页码：299 / 313

页数：15

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