A survey of anomaly detection techniques in financial domain

Ahmed, Mohiuddin; Mahmood, Abdun Naser; Islam, Md. Rafiqul

doi:10.1016/j.future.2015.01.001

Cited by 345 publications

(161 citation statements)

References 31 publications

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“…Z této oblasti můžeme uvést článek zabývající se detekcí anomálií v big data z oblasti financí (Mohiuddin, Shahadat, 2017) nebo přehledový článek o různých metodách detekce anomálií ve finanční doméně (Mohiuddin et al, 2016).…”

Section: Porovnání S Existujícím Výzkumemunclassified

Data Mining from Open Fiscal Data

Chudán¹,

Svátek²,

Kuchař³

et al. 2018

AIP

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AbstraktMetody dolování z dat jsou aplikovány ve stále větší míře, a to i v doménách, které tradičně nemají tak silnou podporu analytických nástrojů a kde převládá ruční práce analytika. Použití těchto metod v oblasti fiskálních dat umožní jejich hlubší analýzu a může přinést nová zjištění. Nasazení pokročilých metod dolování z dat je jednou z částí projektu OpenBudgets.eu, který se zaměřuje na transparentnost a odpovědnost v oblasti nakládání s veřejnými prostředky. Tento přehledový článek shrnuje některé zkušenosti autorů z tohoto projektu získané při vývoji, implementaci a aplikaci vybraných metod dolování z fiskálních dat. Jedná se zejména o metody detekce anomálií a dolování asociačních pravidel. Tyto metody jsou integrovány do centrální platformy projektu, která je k dispozici pokročilým i běžným uživatelům v případě zájmu o analýzu fiskálních dat. Pilotní analýzy ukázaly, že problémem dataminingové analýzy v této doméně je velký objem nacházených pravidel a různorodý původ jejich vzniku.Klíčová slova: Dolování z dat, otevřená data, fiskální data, evropský projekt, OpenBudgets.eu. AbstractData mining methods are still more popular, even in domains where there is traditionally limited support by analytical tools and where the analyst´s manual work still prevails. Using these methods in the fiscal domain enables deeper analysis and can bring new findings. The deployment of data mining methods is one part of the OpenBudgets.eu project, which focuses on transparency and accountability in the public funds management. This overview article summarizes selected experiences of the authors of the project from the development, implementation and application of selected data mining methods on mining fiscal data. These methods are integrated into the central platform of the project available for the advanced and common users interested in fiscal data analysis. The pilot analysis showed that the problem of data mining in this domain is the large amount of found rules together with its heterogenous origin.

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Section: Porovnání S Existujícím Výzkumemunclassified

Data Mining from Open Fiscal Data

Chudán¹,

Svátek²,

Kuchař³

et al. 2018

AIP

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“…For a familiar example, we can consider expenditure on electricity bills. If the usual bill per month is about 100 dollars, and if for one month it is 500 dollars then obviously it is a point anomaly [3].…”

Section: Anomaly Detection Fundamentalsmentioning

confidence: 99%

An Investigation of Performance Analysis of Anomaly Detection Techniques for Big Data in SCADA Systems

Ahmed¹,

Anwar²,

Mahmood³

et al. 2015

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Anomaly detection is an important aspect of data mining, where the main objective is to identify anomalous or unusual data from a given dataset. However, there is no formal categorization of application-specific anomaly detection techniques for big data and this ignites a confusion for the data miners. In this paper, we categorise anomaly detection techniques based on nearest neighbours, clustering and statistical approaches and investigate the performance analysis of these techniques in critical infrastructure applications such as SCADA systems. Extensive experimental analysis is conducted to compare representative algorithms from each of the categories using seven benchmark datasets (both real and simulated) in SCADA systems. The effectiveness of the representative algorithms is measured through a number of metrics. We highlighted the set of algorithms that are the best performing for SCADA systems.

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“…It is an interesting area of data mining research as it involves discovering new and rare patterns in a dataset. It has been widely studied in statistics and machine learning, and is also known as outlier detection, novelty detection, deviation detection and exception mining [25][26][27].…”

Section: Anomaly Detectionmentioning

confidence: 99%

Novel Approach for Network Traffic Pattern Analysis using Clustering-based Collective Anomaly Detection

Ahmed

Mahmood

2015

Ann. Data. Sci.

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There is increasing interest in the data mining and network management communities in improving existing techniques for the prompt analysis of underlying traffic patterns. Anomaly detection is one such technique for detecting abnormalities in many different domains, such as computer network intrusion, gene expression analysis, financial fraud detection and many more. Clustering is a useful unsupervised method for both identifying underlying patterns in data and anomaly detection. However, existing clustering-based techniques have high false alarm rates and consider only individual data instances for anomaly detection. Interestingly, there are traffic flows which seem legitimate but are targeted at disrupting a normal computing environment, such as the Denial of Service (DoS) attack. The presence of such anomalous data instances explains the poor performances of existing clustering-based anomaly detection techniques. In this paper, we formulate the problem of detecting DoS attacks as a collective anomaly which is a pattern in the data when a group of similar data instances behave anomalously with respect to the entire dataset. We propose a framework for collective anomaly detection using a partitional clustering technique to detect anomalies based on an empirical analysis of an attack's characteristics. We validate our approach by comparing its results with those from existing techniques using benchmark datasets.

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A survey of anomaly detection techniques in financial domain

Cited by 345 publications

References 31 publications

Data Mining from Open Fiscal Data

Data Mining from Open Fiscal Data

An Investigation of Performance Analysis of Anomaly Detection Techniques for Big Data in SCADA Systems

Novel Approach for Network Traffic Pattern Analysis using Clustering-based Collective Anomaly Detection

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