Anomaly Detection for Fraud in Cryptocurrency Time Series

07/23/2022
by   Eran Kaufman, et al.
0

Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond initial expectations as daily trades exceed 10 billion. As industries become automated, the need for an automated fraud detector becomes very apparent. Detecting anomalies in real time prevents potential accidents and economic losses. Anomaly detection in multivariate time series data poses a particular challenge because it requires simultaneous consideration of temporal dependencies and relationships between variables. Identifying an anomaly in real time is not an easy task specifically because of the exact anomalistic behavior they observe. Some points may present pointwise global or local anomalistic behavior, while others may be anomalistic due to their frequency or seasonal behavior or due to a change in the trend. In this paper we suggested working on real time series of trades of Ethereum from specific accounts and surveyed a large variety of different algorithms traditional and new. We categorized them according to the strategy and the anomalistic behavior which they search and showed that when bundling them together to different groups, they can prove to be a good real-time detector with an alarm time of no longer than a few seconds and with very high confidence.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/01/2023

RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series

An open-ended time series refers to a series of data points indexed in t...
research
08/30/2023

Classification of Anomalies in Telecommunication Network KPI Time Series

The increasing complexity and scale of telecommunication networks have l...
research
05/25/2023

RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series

A multivariate time series refers to observations of two or more variabl...
research
07/19/2021

OnlineSTL: Scaling Time Series Decomposition by 100x

Decomposing a complex time series into trend, seasonality, and remainder...
research
02/20/2023

CNTS: Cooperative Network for Time Series

The use of deep learning techniques in detecting anomalies in time serie...
research
09/23/2022

A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics

Timely and accurate detection of anomalies in power electronics is becom...
research
09/18/2019

Real-time Recognition of Smartphone User Behavior Based on Prophet Algorithms

Although the traditional physical password, fingerprint unlocking and fa...

Please sign up or login with your details

Forgot password? Click here to reset