Privacy-Preserving Distributed Event Correlation
Creators: name: Parekh, Janak J.
Publisher: name: Columbia University
Year: 2005
Subjects: Computer science

description: Event correlation is a widely-used data processing methodology for a broad variety of applications, and is especially useful in the context of distributed monitoring for software faults and vulnerabilities. However, most existing solutions have typically been focused on 'intra-organizational' correlation; organizations typically employ privacy policies that prohibit the exchange of information outside of the organization. At the same time, the promise of 'inter-organizational' correlation is significant given the broad availability of Internet-scale communications, and its potential role in both software maintenance and software vulnerability exploits. In this proposal, I present a framework for reconciling these opposing forces in event correlation via the use of privacy preservation integrated into the event processing framework. By integrating flexible privacy policies, we enable the correlation of organizations' data without actually releasing sensitive information. The framework supports both source anonymity and data privacy, yet allows for the time-based correlation of a broad variety of data. The framework is designed as a lightweight collection of components to enable integration with existing COTS platforms and distributed systems. I also present two different implementations of this framework: XUES (XML Universal Event Service), an event processor used as part of a software monitoring platform called KX (Kinesthetics eXtreme), and Worminator, a collaborative Intrusion Detection System. KX comprised a series of components, connected together with a publish-subscribe content-based routing event subsystem, for the autonomic software monitoring of complex distributed systems. Sensors were installed in legacy systems. XUES' two modules then performed event processing on sensor data: information was collected and processed by the Event Packager, and correlated using the Event Distiller. While XUES itself was not privacy-preserving, it laid the groundwork for this thesis by supporting event typing, the use of publish-subscribe and extensibility support via pluggable event transformation modules. Worminator, the second implementation, extends the XUES platform to fully support privacy-preserving event types and algorithms in the context of a Collaborative Intrusion Detection System (CIDS), whereby sensor alerts can be exchanged and corroborated--a reduced form of correlation that enables collaborative verification--without revealing sensitive information about a contributor's network, services, or even external sources as required. Worminator also fully anonymizes source information, allowing contributors to decide their preferred level of information disclosure. Worminator is implemented as a monitoring framework on top of a COTS IDS sensor, and demonstrably enables the detection of not only worms but also 'broad and stealthy' scans; traditional single-network sensors either bury such scans in large volumes or miss them entirely. Worminator has been successfully deployed at 5 collaborating sites and work is under way to scale it up further. The contributions of this thesis include the development of a cross-application-domain event correlation framework with native privacy-preserving types, the use and validation of privacy-preserving corroboration, and the establishment of a practical deployed collaborative security system. I also outline the next steps in the thesis research plan, including the development of evaluation metrics to quantify Worminator's effectiveness at long-term scan detection, the overhead of privacy preservation and the effectiveness of our approach against adversaries, be they 'honest-but-curious' or actively malicious. This thesis has broad future work implications, including privacy-preserving signature detection and distribution, distributed stealthy attacker profiling, and 'application community'-based software vulnerability detection.