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Often logs hosted in large data centers represent network traffic data over along period of time. For instance, such network traffic data logged via a TCPdump packet sniffer (as considered in the 1998 DARPA intrusion attack) includednetwork packets being transmitted between computers. While an online frameworkis necessary for detecting any anomalous or suspicious network activities likedenial of service attacks or unauthorized usage in real time, often such largedata centers log data over long periods of time (e.g., TCP dump) and hence anoffline framework is much more suitable in such scenarios. Given a network loghistory of edges from a dynamic graph, how can we assign anomaly scores toindividual edges indicating suspicious events with high accuracy using onlyconstant memory and within limited time than state-of-the-art methods? Wepropose MDistrib and its variants which provides (a) faster detection ofanomalous events via distributed processing with GPU support compared to otherapproaches, (b) better false positive guarantees than state of the art methodsconsidering fixed space and (c) with collision aware based anomaly scoring forbetter accuracy results than state-of-the-art approaches. We describeexperiments confirming that MDistrib is more efficient than prior work.

Distributed Anomaly Detection in Edge Streams using Frequency based Sketch Datastructures