Visible to the public Exploring the Dark Web for Cyber Threat Intelligence Using Machine Leaning

TitleExploring the Dark Web for Cyber Threat Intelligence Using Machine Leaning
Publication TypeConference Paper
Year of Publication2019
AuthorsKADOGUCHI, Masashi, HAYASHI, Shota, HASHIMOTO, Masaki, OTSUKA, Akira
Conference Name2019 IEEE International Conference on Intelligence and Security Informatics (ISI)
Date PublishedJuly 2019
PublisherIEEE
ISBN Number978-1-7281-2504-6
Keywordsartificial intelligence security, composability, Computer crime, counter measure, cyber attack techniques, Cyber Attacks, cyber intelligence, cyber threat intelligence, cyberattack, dark web, darkweb, Data models, Doc2Vec, Information security, intelligence, learning (artificial intelligence), machine leaning, machine learning, Malware, natural language processing, privacy, pubcrawl, resilience, Resiliency, Tools
Abstract

In recent years, cyber attack techniques are increasingly sophisticated, and blocking the attack is more and more difficult, even if a kind of counter measure or another is taken. In order for a successful handling of this situation, it is crucial to have a prediction of cyber attacks, appropriate precautions, and effective utilization of cyber intelligence that enables these actions. Malicious hackers share various kinds of information through particular communities such as the dark web, indicating that a great deal of intelligence exists in cyberspace. This paper focuses on forums on the dark web and proposes an approach to extract forums which include important information or intelligence from huge amounts of forums and identify traits of each forum using methodologies such as machine learning, natural language processing and so on. This approach will allow us to grasp the emerging threats in cyberspace and take appropriate measures against malicious activities.

URLhttps://ieeexplore.ieee.org/document/8823360
DOI10.1109/ISI.2019.8823360
Citation Keykadoguchi_exploring_2019