Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances

Hilal, Waleed; Gadsden, S. Andrew; Yawney, John

doi:10.1016/j.eswa.2021.116429

Cited by 122 publications

(23 citation statements)

References 99 publications

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“…Recently, in the literature, there has been a growing interest in the task of monitoring user behaviour and actions and using machine learning-based approaches to analyse the resulting logs in order to minimise or prevent cybersecurity risks or frauds [16] [1].…”

Section: Related Workmentioning

confidence: 99%

A Scalable Cybersecurity Framework for Anomaly Detection in User Behaviour

Folino¹,

Godano²,

Pisani³

2022

Preprint

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Nowadays, the speed of the user and application logs is so quick that it is almost impossible to analyse them in real-time without using scalable systems and platforms. In cybersecurity, human behaviour is responsible directly or indirectly for the most common attacks (i.e., ransomware and phishing). To monitor user behaviour, it is necessary to process fast user logs coming from different and heterogeneous sources, having part of the data or some entire sources missing. A scalable framework based on the Elastic Stack (ELK) to process and store log data from different users and applications is proposed for this aim. This system generates an ensemble of models to classify user behaviour and detect anomalies in real-time. The scalability of the system is guaranteed by the ELK-based software architecture, running on top of a Kubernetes platform. In addition, a distributed evolutionary algorithm is used to classify the users by exploiting their digital footprints derived from many data sources. Experiments conducted on two real-life datasets verify the approach's goodness in detecting anomalies in the user behaviour, coping with missing data and lowering the number of false alarms.

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Section: Related Workmentioning

confidence: 99%

A Scalable Cybersecurity Framework for Anomaly Detection in User Behaviour

Folino¹,

Godano²,

Pisani³

2022

Preprint

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“…Meanwhile, fraud is a crime committed in a computer-based system or internet network that aims to manipulate financial information in order to make as much profit as possible (Mao, Sun, Zhu, & Li, 2022). Cyber fraud is a crime committed in a computer-based system or internet network that aims to manipulate financial information in order to make maximum profits (Hilal, Gadsden, & Yawney, 2022).…”

Section: Definition Of Cyber Fraudmentioning

confidence: 99%

Cyber Crime Fraud in Jakarta and Tangerang

Zakkiandri

Karsam

Budiandru

2022

103

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The purpose of this study is to determine Cyber Security, Cyber Hardware, Information Systems against Fraud. The population of this study are users of information technology in Jakarta and Tangerang. This research method is a qualitative method and the type of primary data by collecting questionnaires. Types of primary data are data taken directly from the object of research, using a questionnaire with a total of 103 respondents. While secondary data is data obtained from documents from banks and other sources related to this research. The sampling technique is the analysis of the outer model (measurement model) and analysis of the inner model (structural model) using the Multivariate Structural Equation Model (SEM) Partial least square or commonly abbreviated as PLS is a type of statistical analysis whose use is similar to SEM in covariance analysis. Because it is similar to SEM, the basic framework in PLS used is based on linear regression. So, what is in linear regression, is also in PLS. It's just that it is given a different symbol, symbol or term. In participatory technique, the results of this study show that cybersecurity, cyber hardware has a positive effect on fraud. The performance of this research shows that Cyber security and cyber-Hardware have an effect on Fraud, so that users or users are more careful in using information technology and always update it. Simultaneously, the R-Square value is 0.257 or 25%, which means cyber security, cyber hardware influenced by the information system against fraud by 25%.

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“…In the realm of data mining, outlier detection is receiving much interest, and has shown its benefits in a wide range of applications including fraud detection [4], cybersecurity [5] and the health sector [6]. Identifying outliers through data sets contributes towards decision support, risk and impact studies.…”

Section: Introductionmentioning

confidence: 99%

Prominent Discord Discovery with Matrix Profile : Application to Climate Data Insights

Khansa¹,

Gervet²,

Brouillet³

2022

Computer Science &Amp; Technology Trends

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The definition and extraction of actionable anomalous discords, i.e. pattern outliers, is a challenging problem in data analysis. It raises the crucial issue of identifying criteria that would render a discord more insightful than another one. In this paper, we propose an approach to address this by introducing the concept of prominent discord. The core idea behind this new concept is to identify dependencies among discords of varying lengths. How can we identify a discord that would be prominent? We propose an ordering relation, that ranks discords and we seek a set of prominent discords with respect to this ordering. Our contributions are 1) a formal definition, ordering relation and methods to derive prominent discords based on Matrix Profile techniques, and 2) their evaluation over large contextual climate data, covering 110 years of monthly data. The approach is generic and its pertinence shown over historical climate data.

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Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances

Cited by 122 publications

References 99 publications

A Scalable Cybersecurity Framework for Anomaly Detection in User Behaviour

A Scalable Cybersecurity Framework for Anomaly Detection in User Behaviour

Cyber Crime Fraud in Jakarta and Tangerang

Prominent Discord Discovery with Matrix Profile : Application to Climate Data Insights

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