BreachRadar: Automatic Detection of Points-of-Compromise

Araujo, Miguel; Almeida, Miguel; Ferreira, Jaime Octavio Albarracín; Silva, Luis; Bizarro, Pedro

doi:10.1137/1.9781611974973.63

Cited by 2 publications

(2 citation statements)

References 9 publications

(9 reference statements)

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“…Araujo et al [54] have recently developed "BreachRadar", a distributed alternating algorithm that assigns a probability of a banking card being compromised to the different possible locations of card use. These Points-of-Compromise (POC) are the point of initiation for fraud in bank transactions.…”

Section: Insider Tradingmentioning

confidence: 99%

Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape

2021

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Machine Learning and Deep Learning methods are widely adopted across financial domains to support trading activities, mobile banking, payments, and making customer credit decisions. These methods also play a vital role in combating financial crime, fraud, and cyberattacks. Financial crime is increasingly being committed over cyberspace, and cybercriminals are using a combination of hacking and social engineering techniques which are bypassing current financial and corporate institution security. With this comes a new umbrella term to capture the evolving landscape which is financial cybercrime. It is a combination of financial crime, hacking, and social engineering committed over cyberspace for the sole purpose of illegal economic gain. Identifying financial cybercrime-related activities is a hard problem, for example, a highly restrictive algorithm may block all suspicious activity obstructing genuine customer business. Navigating and identifying legitimate illicit transactions is not the only issue faced by financial institutions, there is a growing demand of transparency, fairness, and privacy from customers and regulators, which imposes unique constraints on the application of artificial intelligence methods to detect fraud-related activities. Traditionally, rule based systems and shallow anomaly detection methods have been applied to detect financial crime and fraud, but recent developments have seen graph based techniques and neural network models being used to tackle financial cybercrime. There is still a lack of a holistic understanding of the financial cybercrime ecosystem, relevant methods, and their drawbacks and new emerging open problems in this domain in spite of their popularity. In this survey, we aim to bridge the gap by studying the financial cybercrime ecosystem based on four axes: (a) different fraud methods adopted by criminals; (b) relevant systems, algorithms, drawbacks, constraints, and metrics used to combat each fraud type; (c) the relevant personas and stakeholders involved; (d) open and emerging problems in the financial cybercrime domain.

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Section: Insider Tradingmentioning

confidence: 99%

Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape

2021

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“…Araujo et al [33] likens the POC detection problem to that of detecting malware in a computer file system and uses a bipartite graph to solve this problem. However; for data privacy concerns, the study does not provide details about the use of datasets.…”

Section: B Point Of Compromise Detectionmentioning

confidence: 99%

Temporal Transaction Scraping Assisted Point of Compromise Detection With Autoencoder Based Feature Engineering

et al. 2021

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Credit card fraudsters exploit various methods to capture card information. One of the common methods is to duplicate the credit cards by skimming. In this study, we introduce a new point of compromise detection method in order to trace and identify merchants where the skimming operation took place and card information has been captured by criminals. The proposed method first extracts discriminative features by using principle component analysis(PCA) and Autoencoder extractors and then it clusters similar fraudulent transactions with K-Means algorithm, afterwards it highlights possible merchants that are involved in this scheme by finding matching merchants in the produced clusters with a retrospective analysis of all transactions. Our experiments showed that the proposed method could achieve promising results with zero-knowledge on the existing skimming points. The application of our proposed method on real-life card transactions enabled us to pinpoint 7 out of 9 point of compromise previously identified by the reporting bank.

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BreachRadar: Automatic Detection of Points-of-Compromise

Cited by 2 publications

References 9 publications

Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape

Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape

Temporal Transaction Scraping Assisted Point of Compromise Detection With Autoencoder Based Feature Engineering

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