Graph based anomaly detection and description: a survey
TLDR
This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs, and gives a general framework for the algorithms categorized under various settings.Abstract:
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised versus (semi-)supervised approaches, for static versus dynamic graphs, for attributed versus plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the `why', of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.read more
Citations
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Proceedings ArticleDOI
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection
TL;DR: In this paper , the authors proposed a new type of targeted structural poisoning attacks to a representative regression-based anomaly detection system termed OddBall, and formulated the attack against OddBall as a bi-level optimization problem, where the key technical challenge is to efficiently solve the problem in a discrete domain.
Scalable Algorithms for Mining Dynamic Graphs and Hypergraphs with Applications to Anomaly Detection.
TL;DR: This dissertation develops anomaly detection algorithms for increasingly complex graph edge stream models and shows their effectiveness, both theoretically and empirically.
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Increasing the Effectiveness of Network Intrusion Detection Systems (NIDSs) by Using Multiplex Networks and Visibility Graphs
TL;DR: In this article , the authors proposed a new approach based on machine learning to detect attackers by analyzing the relationship between computers over time, which can reduce the number of alerts generated by NIDS deployment.
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Knowledge distillation on neural networks for evolving graphs
TL;DR: DynGKD as discussed by the authors proposes a distillation strategy to transfer the knowledge from a large teacher model to a small student model with low inference latency, while achieving high prediction accuracy.
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An Abnormal Data Analysis and Processing Method for Genealogy Graph Databases
TL;DR: To avoid abnormal data in genealogy graph database, the abnormal data are categorized into four different types, and the corresponding processing methods are proposed for each type of abnormal data, respectively.
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