Online mining for association rules and collective anomalies in data streams

Abbady, Shaaban; Ke, Cheng-Yuan; Lavergne, Jennifer; Chen, Jian; Raghavan, Vijay V.; Benton, Ryan

doi:10.1109/bigdata.2017.8258192

Cited by 4 publications

(1 citation statement)

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“…This is often insufficient when determining if a system has been compromised. Hence, the data would need to be feed into online systems [52], where anomaly detection methods could be used to detected unexpected behavior. This can be done with using methods such as simple learning systems, such as the Hierarchical Temporal Memory method [53], DBScan [54], or generalized linear models [55].…”

Section: Discussionmentioning

confidence: 99%

Insight from a Docker Container Introspection

Watts

Benton

Glisson

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Large-scale adoption of virtual containers has stimulated concerns by practitioners and academics about the viability of data acquisition and reliability due to the decreasing window to gather relevant data points. These concerns prompted the idea that introspection tools, which are able to acquire data from a system as it is running, can be utilized as both an early warning system to protect that system and as a data capture system that collects data that would be valuable from a digital forensic perspective. An exploratory case study was conducted utilizing a Docker engine and Prometheus as the introspection tool. The research contribution of this research is twofold. First, it provides empirical support for the idea that introspection tools can be utilized to ascertain differences between pristine and infected containers. Second, it provides the ground work for future research conducting an analysis of large-scale containerized applications in a virtual cloud.

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Section: Discussionmentioning

confidence: 99%