AugSplicing: Synchronized Behavior Detection in Streaming Tensors

• URL: http://arxiv.org/abs/2012.02006v5
• Date: Tue, 30 Mar 2021 14:42:58 GMT
• Title: AugSplicing: Synchronized Behavior Detection in Streaming Tensors
• Authors: Jiabao Zhang, Shenghua Liu, Wenting Hou, Siddharth Bhatia, Huawei Shen, Wenjian Yu, Xueqi Cheng
• Abstract summary: We propose a fast streaming algorithm, AugSplicing, which can detect dense blocks by splicing the previous detection with incoming ones in news. 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 group of students with interesting features in campus Wi-Fi data.
• Score: 38.90084196554039
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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