ATT&CK Behavior Forecasting based on Collaborative Filtering and Graph Databases

Masaki Kuwano, Momoka Okuma, Satoshi Okada, Takuho Mitsunaga
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引用次数: 0

Abstract

Cyber attacks are causing tremendous damage around the world. To protect against attacks, many organizations have established or outsourced Security Operation Centers (SOCs) to check a large number of logs daily. Since there is no perfect countermeasure against cyber attacks, it is necessary to detect signs of intrusion quickly to mitigate damage caused by them. However, it is challenging to analyze a lot of logs obtained from PCs and servers inside an organization. Therefore, there is a need for a method of efficiently analyzing logs. In this paper, we propose a recommendation system using the ATT&CK technique, which predicts and visualizes attackers’ behaviors using collaborative filtering so that security analysts can analyze logs efficiently.
基于协同过滤和图数据库的at&ck行为预测
网络攻击在世界范围内造成了巨大的破坏。为了防止攻击,许多组织已经建立或外包了安全操作中心(soc),每天检查大量的日志。由于没有完美的应对网络攻击的对策,因此有必要迅速发现入侵迹象,以减轻其造成的损害。然而,分析从组织内的pc和服务器获得的大量日志是一项挑战。因此,需要一种有效分析日志的方法。本文提出了一种基于ATT&CK技术的推荐系统,该系统通过协同过滤对攻击者的行为进行预测和可视化,使安全分析人员能够有效地分析日志。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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