Sometimes, You Aren't What You Do: Mimicry Attacks against Provenance Graph Host Intrusion Detection Systems
Sometimes, You Aren't What You Do: Mimicry Attacks against Provenance Graph Host Intrusion Detection Systems
复制标题
DOI：
10.14722/ndss.2023.24207
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Akul Goyal;Xueyuan Han;G. Wang;Adam Bates
中科院分区：
文献类型：
--
作者：
Akul Goyal;Xueyuan Han;G. Wang;Adam Bates
主机层入侵检测的可靠方法仍然是计算机安全领域的一个悬而未决的问题。最近的研究将入侵检测重塑为起源图异常检测问题，这要归功于机器学习和因果图审计的同时发展。虽然这些方法显示出了希望，但它们对抗适应性对手的稳健性尚未得到证明。特别是，目前尚不清楚过去困扰主机入侵检测方法的模仿攻击是否对现代基于图形的方法具有类似的影响。在这项工作中，我们揭示了系统化的设计选择使得模仿攻击在起源图主机入侵检测系统(Prov-HID)中继续大量存在。针对样本Prov-HID语料库，我们开发了规避策略，允许攻击者隐藏在良性进程行为中。通过对公共数据集的评估，我们证明了攻击者可以在不修改底层攻击行为的情况下一致地逃避检测(100%成功率)。我们进一步证明了我们的方法在实时攻击场景中是可行的，并且性能优于领域通用对手样本技术。通过开源我们的代码和数据集，这项工作将成为评估未来PROV-HID的基准。
Reliable methods for host-layer intrusion detection remained an open problem within computer security. Recent research has recast intrusion detection as a provenance graph anomaly detection problem thanks to concurrent advancements in machine learning and causal graph auditing. While these approaches show promise, their robustness against an adaptive adversary has yet to be proven. In particular, it is unclear if mimicry attacks, which plagued past approaches to host intrusion detection, have a similar effect on modern graph-based methods. In this work, we reveal that systematic design choices have allowed mimicry attacks to continue to abound in provenance graph host intrusion detection systems (Prov-HIDS). Against a corpus of exemplar Prov-HIDS, we develop evasion tactics that allow attackers to hide within benign process behaviors. Evaluating against public datasets, we demonstrate that an attacker can consistently evade detection (100% success rate) without modifying the underlying attack behaviors. We go on to show that our approach is feasible in live attack scenarios and outperforms domain-general adversarial sample techniques. Through open sourcing our code and datasets, this work will serve as a benchmark for the evaluation of future Prov-HIDS.