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
More filters
Proceedings ArticleDOI
REV2: Fraudulent User Prediction in Rating Platforms
TL;DR: The REV2 algorithm is developed, a system to identify fraudulent users and outperforms nine existing algorithms in detecting fair and unfair users and is guaranteed to converge and has linear time complexity.
Journal ArticleDOI
APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions
Véronique Van Vlasselaer,Cristián Bravo,Olivier Caelen,Tina Eliassi-Rad,Leman Akoglu,Monique Snoeck,Bart Baesens,Bart Baesens +7 more
TL;DR: APATE is proposed, a novel approach to detect fraudulent credit card transactions conducted in online stores that combines intrinsic features derived from the characteristics of incoming transactions and the customer spending history using the fundamentals of RFM.
Journal ArticleDOI
Real-time big data processing for anomaly detection: A Survey
Riyaz Ahamed Ariyaluran Habeeb,Fariza Hanum Nasaruddin,Abdullah Gani,Ibrahim Abaker Targio Hashem,Ejaz Ahmed,Muhammad Imran +5 more
TL;DR: This paper begins with the explanation of essential contexts and taxonomy of real-time big dataprocessing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies.
Journal ArticleDOI
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff,Jacob R. Kauffmann,Robert A. Vandermeulen,Grégoire Montavon,Wojciech Samek,Marius Kloft,Thomas G. Dietterich,Klaus-Robert Müller +7 more
TL;DR: Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text as mentioned in this paper, and led to the introduction of a great variety of new methods.
Journal ArticleDOI
Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey
TL;DR: An extensive review of the many different works in the field of software vulnerability analysis and discovery that utilize machine-learning and data-mining techniques that utilize both advantages and shortcomings in this domain is provided.
References
More filters
Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.