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Enterprise networks are one of the major targets for cyber attacks due to thevast amount of sensitive and valuable data they contain. A common approach todetecting attacks in the enterprise environment relies on modeling the behaviorof users and systems to identify unexpected deviations. The feasibility of thisapproach crucially depends on how well attack-related events can be isolatedfrom benign and mundane system activities. Despite the significant focus onend-user systems, the background behavior of servers running critical servicesfor the enterprise is less studied. To guide the design of detection methodstailored for servers, in this work, we examine system event records from 46servers in a large enterprise obtained over a duration of ten weeks. We analyzethe rareness characteristics and the similarity of the provenance relations inthe event log data. Our findings show that server activity, in general, ishighly variant over time and dissimilar across different types of servers.However, careful consideration of profiling window of historical events andservice level grouping of servers improve rareness measurements by 24.5%.Further, utilizing better contextual representations, the similarity inprovenance relationships could be improved. An important implication of ourfindings is that detection techniques developed considering experimental setupswith non-representative characteristics may perform poorly in practice.

Exploration of Enterprise Server Data to Assess Ease of Modeling System Behavior