利用机器学习探索暗网中的网络威胁情报

Masashi Kadoguchi, S. Hayashi, Masaki Hashimoto, Akira Otsuka
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引用次数: 23

摘要

近年来,网络攻击技术越来越复杂,即使采取这样或那样的反制措施,阻止攻击也越来越困难。为了成功处理这种情况,对网络攻击进行预测、采取适当的预防措施以及有效利用网络情报来实现这些行动至关重要。恶意黑客通过暗网等特定社区共享各种信息,表明网络空间中存在大量智能。本文以暗网论坛为研究对象,提出了一种利用机器学习、自然语言处理等方法从海量论坛中提取包含重要信息或智能的论坛,并识别每个论坛特征的方法。这将使我们能够掌握网络空间新出现的威胁,并对恶意活动采取适当措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Dark Web for Cyber Threat Intelligence using Machine Leaning
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.
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