Detecting Group Anomalies in Tera-Scale Multi-Aspect Data via Dense-Subtensor Mining

TL;DR

This paper introduces D-CUBE, a disk-based and distributed method for detecting dense subtensors in large-scale multi-aspect data, significantly improving memory efficiency, speed, and accuracy over existing approaches.

Contribution

The paper presents D-CUBE, a novel scalable and memory-efficient algorithm for dense-subtensor detection in massive tensors, with provable accuracy and practical effectiveness.

Findings

Handles 1,000X larger data than previous methods

Up to 7X faster processing speed

Achieves high accuracy in detecting network attacks and synchronized behaviors

Abstract

How can we detect fraudulent lockstep behavior in large-scale multi-aspect data (i.e., tensors)? Can we detect it when data are too large to fit in memory or even on a disk? Past studies have shown that dense subtensors in real-world tensors (e.g., social media, Wikipedia, TCP dumps, etc.) signal anomalous or fraudulent behavior such as retweet boosting, bot activities, and network attacks. Thus, various approaches, including tensor decomposition and search, have been proposed for detecting dense subtensors rapidly and accurately. However, existing methods have low accuracy, or they assume that tensors are small enough to fit in main memory, which is unrealistic in many real-world applications such as social media and web. To overcome these limitations, we propose D-CUBE, a disk-based dense-subtensor detection method, which also can run in a distributed manner across multiple machines. Compared to state-of-the-art methods, D-CUBE is (1) Memory Efficient: requires up to 1,561X less memory and handles 1,000X larger data (2.6TB), (2) Fast: up to 7X faster due to its near-linear scalability, (3) Provably Accurate: gives a guarantee on the densities of the detected subtensors, and (4) Effective: spotted network attacks from TCP dumps and synchronized behavior in rating data most accurately.