AugSplicing: Synchronized Behavior Detection in Streaming Tensors

TL;DR

AugSplicing is a fast streaming algorithm designed to detect dense blocks indicating synchronized or anomalous behavior in streaming tensor data, improving efficiency and robustness over existing methods.

Contribution

It introduces a novel splicing-based streaming algorithm for dense block detection in tensors, enabling real-time analysis without reprocessing entire data history.

Findings

Effective in detecting fraudulent app installation behavior

Identifies synchronized student groups in Wi-Fi data

Faster and more robust than existing streaming algorithms

Abstract

How can we track synchronized behavior in a stream of time-stamped tuples, such as mobile devices installing and uninstalling applications in the lockstep, to boost their ranks in the app store? We model such tuples as entries in a streaming tensor, which augments attribute sizes in its modes over time. Synchronized behavior tends to form dense blocks (i.e. subtensors) in such a tensor, signaling anomalous behavior, or interesting communities. However, existing dense block detection methods are either based on a static tensor, or lack an efficient algorithm in a streaming setting. Therefore, we propose a fast streaming algorithm, AugSplicing, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step. AugSplicing is based on a splicing condition that guides the algorithm (Section 4). Compared to the state-of-the-art methods, our method is (1) effective to detect fraudulent behavior in installing data of real-world apps and find a synchronized group of students with interesting features in campus Wi-Fi data; (2) robust with splicing theory for dense block detection; (3) streaming and faster than the existing streaming algorithm, with closely comparable accuracy.