GRAIN:图神经网络和强化学习辅助因果关系发现,用于多步骤攻击场景重建

IF 4.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Fengrui Xiao , Shuangwu Chen , Jian Yang , Huasen He , Xiaofeng Jiang , Xiaobin Tan , Dong Jin
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引用次数: 0

摘要

关联单个警报以重构攻击场景已成为识别多步骤攻击路径的关键问题。现有的大多数重构方法都依赖于外部专业知识(如攻击模板或攻击图)来识别已知的攻击模式,但这些方法无法发现超出先前知识范围的未知攻击模式。最近,一些独立于专业知识的方法利用警报相似性或统计相关性来重建多步骤攻击。然而,这些方法往往会错过罕见但高风险的事件。克服这些缺点的关键在于发现安全警报之间的潜在因果关系。在本文中,我们提出了一种新型图神经网络和强化学习辅助因果关系发现方法 GRAIN,用于多步骤攻击场景重构,这种方法不依赖任何外部专业知识或先验知识。通过匹配警报的攻击语义之间的相似性,我们首先删除冗余警报,以减轻警报疲劳。然后,我们将这些警报关联为警报因果图,通过因果关系发现体现攻击事件之间的因果关系。之后,我们采用图神经网络来评估相关警报之间的因果效应。鉴于多步骤攻击触发的警报具有最大的因果效应,我们利用强化学习来筛选出真实的因果关系。在 4 个公开的多步骤攻击数据集上进行的广泛评估表明,GRAIN 在准确性和效率方面明显优于现有方法,为识别和分析复杂的多步骤攻击提供了强大的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GRAIN: Graph neural network and reinforcement learning aided causality discovery for multi-step attack scenario reconstruction
Correlating individual alerts to reconstruct attack scenarios has become a critical issue in identifying multi-step attack paths. Most of existing reconstruction approaches depend on external expertise, such as attack templates or attack graphs, to identify known attack patterns, which are incapable of uncovering unknown attack patterns that exceed prior knowledge. Recently, several expertise-independent methods utilize alert similarity or statistical correlations to reconstruct multi-step attacks. However, these methods often miss rare but high-risk events. The key to overcoming these drawbacks lies in discovering the potential causalities between security alerts. In this paper, we propose GRAIN, a novel graph neural network and reinforcement learning aided causality discovery approach for multi-step attack scenario reconstruction, which does not rely on any external expertise or prior knowledge. By matching the similarity between alerts’ attack semantics, we first remove redundant alerts to alleviate alert fatigue. Then, we correlate these alerts as alert causal graphs that embody the causalities between attack incidents via causality discovery. Afterwards, we employ a graph neural network to evaluate the causal effect between correlated alerts. In light of the fact that the alerts triggered by multi-step attacks have the maximum causal effect, we utilize reinforcement learning to screen out authentic causal relationships. Extensive evaluations on 4 public multi-step attack datasets demonstrate that GRAIN significantly outperforms existing methods in terms of accuracy and efficiency, providing a robust solution for identifying and analyzing sophisticated multi-step attacks.
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来源期刊
Computers & Security
Computers & Security 工程技术-计算机:信息系统
CiteScore
12.40
自引率
7.10%
发文量
365
审稿时长
10.7 months
期刊介绍: Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.
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