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Sanjeev R. Kulkarni works at Princeton University.

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A Query System for Efficiently Investigating Complex Attack Behaviors for Enterprise Security

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The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each enterprise host, and perform timely attack investigation over the monitoring data for uncovering the attack sequence. However, existing general-purpose query systems lack explicit language constructs for expressing key properties of major attack behaviors, and their semantics-agnostic design often produces inefficient execution plans for queries. To address these limitations, we build Aiql, a novel query system that is designed with novel types of domain-specific optimizations to enable efficient attack investigation. Aiql provides (1) a domain-specific data model and storage for storing the massive system monitoring data, (2) a domain-specific query language, Attack Investigation Query Language (Aiql) that integrates critical primitives for expressing major attack behaviors, and (3) an optimized query engine based on the characteristics of the data and the semantics of the query to efficiently schedule the execution. We have deployed Aiql in NEC Labs America comprising 150 hosts. In our demo, we aim to show the complete usage scenario of Aiql by (1) performing an APT attack in a controlled environment, and (2) using Aiql to investigate such attack by querying the collected system monitoring data that contains the attack traces. The audience will have the option to perform the APT attack themselves under our guidance, and interact with the system and investigate the attack via issuing queries and checking the query results through our web UI.

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SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection

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Recently, advanced cyber attacks, which consist of a sequence of steps that involve many vulnerabilities and hosts, compromise the security of many well-protected businesses. This has led to solutions that ubiquitously monitor system activities in each host (big data) as a series of events and search for anomalies (abnormal behaviors) for triaging risky events. Since fighting against these attacks is a time-critical mission to prevent further damage, these solutions face challenges in incorporating expert knowledge to perform timely anomaly detection over the large-scale provenance data. To address these challenges, we propose a novel stream-based query system that takes as input, a real-time event feed aggregated from multiple hosts in an enterprise, and provides an anomaly query engine that queries the event feed to identify abnormal behaviors based on the specified anomalies. To facilitate the task of expressing anomalies based on expert knowledge, our system provides a domain-specific query language, SAQL, which allows analysts to express models for (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-based anomalies, and (4) outlier-based anomalies. We deployed our system in NEC Labs America, comprising 150 hosts, and evaluated it using 1.1TB of real system monitoring data (containing 3.3 billion events). Our evaluations on a broad set of attack behaviors and micro-benchmarks show that our system has a low detection latency (<2s) and a high system throughput (110,000 events/s; supporting ~4000 hosts), and is more efficient in memory utilization than the existing stream-based complex event processing systems.

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AIQL: Enabling Efficient Attack Investigation from System Monitoring Data

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The need for countering Advanced Persistent Threat (APT) attacks has led to 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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