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Dependency graph, as a heterogeneous graph representing the intrinsicrelationships between different pairs of system entities, is essential to manydata analysis applications, such as root cause diagnosis, intrusion detection,etc. Given a well-trained dependency graph from a source domain and an immaturedependency graph from a target domain, how can we extract the entity anddependency knowledge from the source to enhance the target? One way is todirectly apply a mature dependency graph learned from a source domain to thetarget domain. But due to the domain variety problem, directly using the sourcedependency graph often can not achieve good performance. Traditional transferlearning methods mainly focus on numerical data and are not applicable.  In this paper, we propose ACRET, a knowledge transfer based model foraccelerating dependency graph learning from heterogeneous categorical eventstreams. In particular, we first propose an entity estimation model to filterout irrelevant entities from the source domain based on entity embedding andmanifold learning. Only the entities with statistically high correlations aretransferred to the target domain. On the surviving entities, we propose adependency construction model for constructing the unbiased dependencyrelationships by solving a two-constraint optimization problem. Theexperimental results on synthetic and real-world datasets demonstrate theeffectiveness and efficiency of ACRET. We also apply ACRET to a real enterprisesecurity system for intrusion detection. Our method is able to achieve superiordetection performance at least 20 days lead lag time in advance with more than70% accuracy.

Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer