Combining Digital Forensic Practices and Database Analysis as an Anti-Money Laundering Strategy for Financial Institutions

Flores, Denys A.; Angelopoulou, Olga; Self, Richard

doi:10.1109/eidwt.2012.22

Cited by 13 publications

(3 citation statements)

References 17 publications

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“…Consumer conduct in speculation actions is complexs incenumerous influences effect it, likewise show that by selecting appropriate scopes, modest DM practices can be functional composed to detect mistrustful ML belongings in speculation doings [10]. Hence, in this paper, present a onestep clustering approach basing on some heuristics from AML experts to improve the performance of our previous solution in the term of running time [13].…”

Section: Related Workmentioning

confidence: 95%

Money Laundering Identification Using Risk and Structural Framework Estimation

Krishnapriya¹

2017

BIJDM

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Money laundering refers to activities that disguise money receive through illegal operations and make them legitimate. It leaves serious consequence that may lead to economy corruption. One such problem consisting large amounts of money transferring through various accounts by the same person or entity is Money Laundering. Money laundering scheme is quite a complex procedure. It receipts some empathetic of the deposit transporting actions at many phases. Detecting money laundering activities is a challenging task due we propose a risk model framework in Structural Money Laundering based on Risk Evolution Detection Framework (SMLRDF). The connection deceitful deal trails a sequence of connected money laundering arrangements, structural money laundering uses sequence matching, social network investigation, and multifaceted happening processing, case-based examination. The context that put on case discount approaches to increasingly lessen the input data set to a knowingly minor size. The context images the summary data to discovery couples of communications through common qualities and performances that are possibly complicated in ML actions. It then applies a clustering method to detect potential Money Laundering (ML groups), then the risk model is used to create a valid and accurate transaction scoring system to be utilized in an ML prevention system. SMLRDFdependent risk modeling, which captures the hidden, and dynamic, relations among none-transacted entities. SMLRDF has components to collect data, run them against business rules and evolution models, run detection algorithms and use social network analysis to connect potential participants.

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Section: Related Workmentioning

confidence: 95%

Money Laundering Identification Using Risk and Structural Framework Estimation

Krishnapriya¹

2017

BIJDM

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“…Nonetheless, these controls cannot protect the database from unauthorised access when it is directly compromised by insiders [8] who take advantage of their credential privileges in order to alter sensitive information. For instance during the CNE database investigation, it was found that malicious employees were responsible of altering database records of political parties [6], even though records and audit trails were not found.…”

Section: B Database Intrusion Detection Module (Db-idm)mentioning

confidence: 98%

“…Second, the lack of security controls at back-end level prevent authorities to find evidence and audit trails to prosecute malicious individuals, some of them employees [8], who illegally use access privileges to alter database records without being discovered. In the previous CNE example, it was found that this database was not prevented from being accessed without permission, being unclear whether or not the user credentials were managed properly, as evidence based on audit logs where not found in the suspicious database [6].…”

Section: Introductionmentioning

confidence: 99%

An authentication and auditing architecture for enhancing security on egovernment services

Flores

2014

2014 First International Conference on eDemocracy &Amp; eGovernment (ICEDEG)

Self Cite

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eGovernment deploys governmental information and services for citizens and general society. As the Internet is being used as underlying platform for information exchange, these services are exposed to data tampering and unauthorised access as main threats against citizen privacy. These issues have been usually tackled by applying controls at application level, making authentication stronger and protecting credentials in transit using digital certificates. However, these efforts to enhance security on governmental web sites have been only focused on what malicious users can do from the outside, and not in what insiders can do to alter data straight on the databases. In fact, the lack of security controls at back-end level hinders every effort to find evidence and investigate events related to credential misuse and data tampering. Moreover, even though attackers can be found and prosecuted, there is no evidence and audit trails on the databases to link illegal activities with identities. In this article, a Salting-Based Authentication Module and a Database Intrusion Detection Module are proposed as enhancements to eGovernment security to provide better authentication and auditing controls.

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Handling Smurfing Through Big Data

Chadha

Kaur

2017

Advances in Intelligent Systems and Computing

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Combining Digital Forensic Practices and Database Analysis as an Anti-Money Laundering Strategy for Financial Institutions

Cited by 13 publications

References 17 publications

Money Laundering Identification Using Risk and Structural Framework Estimation

Money Laundering Identification Using Risk and Structural Framework Estimation

An authentication and auditing architecture for enhancing security on egovernment services

Handling Smurfing Through Big Data

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