[HTML][HTML] Fraud detection: A systematic literature review of graph-based anomaly detection approaches
Graph-based anomaly detection (GBAD) approaches are among the most popular
techniques used to analyze connectivity patterns in communication networks and identify …
techniques used to analyze connectivity patterns in communication networks and identify …
Deep anomaly detection with deviation networks
Although deep learning has been applied to successfully address many data mining
problems, relatively limited work has been done on deep learning for anomaly detection …
problems, relatively limited work has been done on deep learning for anomaly detection …
Graph based anomaly detection and description: a survey
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 …
such as security, finance, health care, and law enforcement. While numerous techniques …
Artificial intelligence co-piloted auditing
This paper proposes the concept of artificial intelligence co-piloted auditing, emphasizing
the collaborative potential of auditors and foundation models in the auditing domain. The …
the collaborative potential of auditors and foundation models in the auditing domain. The …
Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
Learning expressive low-dimensional representations of ultrahigh-dimensional data, eg,
data with thousands/millions of features, has been a major way to enable learning methods …
data with thousands/millions of features, has been a major way to enable learning methods …
Opinion fraud detection in online reviews by network effects
L Akoglu, R Chandy, C Faloutsos - … AAAI conference on web and social …, 2013 - ojs.aaai.org
User-generated online reviews can play a significant role in the success of retail products,
hotels, restaurants, etc. However, review systems are often targeted by opinion spammers …
hotels, restaurants, etc. However, review systems are often targeted by opinion spammers …
Explainable deep few-shot anomaly detection with deviation networks
Existing anomaly detection paradigms overwhelmingly focus on training detection models
using exclusively normal data or unlabeled data (mostly normal samples). One notorious …
using exclusively normal data or unlabeled data (mostly normal samples). One notorious …
DeltaCon: A Principled Massive-Graph Similarity Function
How much did a network change since yesterday? How different is the wiring between Bob's
brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with …
brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with …
Gotcha! network-based fraud detection for social security fraud
V Van Vlasselaer, T Eliassi-Rad… - Management …, 2017 - pubsonline.informs.org
We study the impact of network information for social security fraud detection. In a social
security system, companies have to pay taxes to the government. This study aims to identify …
security system, companies have to pay taxes to the government. This study aims to identify …
Deep weakly-supervised anomaly detection
Recent semi-supervised anomaly detection methods that are trained using small labeled
anomaly examples and large unlabeled data (mostly normal data) have shown largely …
anomaly examples and large unlabeled data (mostly normal data) have shown largely …