AIQL: Enabling Efficient Attack Investigation from System Monitoring Data


Peng Gao, Princeton University; Xusheng Xiao, Case Western Reserve University; Zhichun Li and Kangkook Jee, NEC Laboratories America, Inc.; Fengyuan Xu, National Key Lab for Novel Software Technology, Nanjing University; Sanjeev R. Kulkarni and Prateek Mittal, Princeton University


The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each host, and perform timely attack investigation over the monitoring data for analyzing attack provenance. However, existing query systems based on relational databases and graph databases lack language constructs to express key properties of major attack behaviors, and often execute queries inefficiently since their semantics-agnostic design cannot exploit the properties of system monitoring data to speed up query execution.

To address this problem, we propose a novel query system built on top of existing monitoring tools and databases, which is designed with novel types of optimizations to support timely attack investigation. Our system provides (1) domain-specific data model and storage for scaling the storage, (2) a domain-specific query language, Attack Investigation Query Language (AIQL) that integrates critical primitives for attack investigation, and (3) an optimized query engine based on the characteristics of the data and the semantics of the queries to efficiently schedule the query execution. We deployed our system in NEC Labs America comprising 150 hosts and evaluated it using 857 GB of real system monitoring data (containing 2.5 billion events). Our evaluations on a real-world APT attack and a broad set of attack behaviors show that our system surpasses existing systems in both efficiency (124x over PostgreSQL, 157x over Neo4j, and 16x over Greenplum) and conciseness (SQL, Neo4j Cypher, and Splunk SPL contain at least 2.4x more constraints than AIQL).

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@inproceedings {215975,
author = {Peng Gao and Xusheng Xiao and Zhichun Li and Fengyuan Xu and Sanjeev R. Kulkarni and Prateek Mittal},
title = {{AIQL}: Enabling Efficient Attack Investigation from System Monitoring Data},
booktitle = {2018 USENIX Annual Technical Conference (USENIX ATC 18)},
year = {2018},
isbn = {ISBN 978-1-939133-01-4},
address = {Boston, MA},
pages = {113--126},
url = {},
publisher = {USENIX Association},
month = jul,

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