Cybersecurity and Fraud Detection in Financial Transactions

Aschi, Massimiliano; Bonura, Susanna; Masi, Nicola; Messina, D; Profeta, Davide

doi:10.1007/978-3-030-94590-9_15

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…This strategy efficiently reduces scam attempts and provides clients with a wisdom of security by uncovering well-known fraud trends. Nonetheless, rule-based detecting fraud technologies have shown in the arena that they are unable to go on with the gradually complex strategies used by cheats to jeopardize important properties: Cybercriminals may readily counteract a set of predetermined levels [6], [7] and fixed criteria are useless for identifying developing risks and adapting to previously undisclosed fraudulent transactions.…”

Section: A Outline Of Extant Banking Fraud Recognition Methods and Th...mentioning

confidence: 99%

End-to-End Real-time Architecture for Fraud Detection in Online Digital Transactions

Hanae,

Abdellah,

Saida

et al. 2023

IJACSA

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The banking sector is witnessing a fierce concurrence characterized by changing business models, new entrants such as FinTechs, and new customer behaviors. Financial institutions try to adapt to this trend and invent new ways and channels to reach and interact with their customers. While banks are opening their services to avoid missing this shift, they become naturally exposed to fraud attempts through their digital banking platforms. Therefore, fraud prevention and detection are considered must-have capabilities. Detecting fraud at an optimal time requires developing and deploying scalable learning systems capable of ingesting and analyzing vast volumes of streaming records. Current improvements in data analytics algorithms and the advent of open-source technologies for big data processing and storage bring up novel avenues for fraud identification. In this article, we provide a real-time architecture for detecting transactional fraud via behavioral analysis that incorporates big data analysis techniques such as Spark, Kafka, and h2o with an unsupervised machine learning (ML) algorithm named Isolation Forest. The results of experiments on a significant dataset of digital transactions indicate that this architecture is robust, effective, and reliable across a large set of transactions yielding 99% of accuracy, and a precision of 87%.

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Section: A Outline Of Extant Banking Fraud Recognition Methods and Th...mentioning

confidence: 99%

End-to-End Real-time Architecture for Fraud Detection in Online Digital Transactions

Hanae,

Abdellah,

Saida

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…In [6], an example of a rule-based expert system (RBESS) was described as a simple implementation of artificial intelligence, which was converted into antifraud rules based on expert knowledge. The authors presented two simple rules and hypothesized that it is better to develop systems based only on artificial intelligence (supervised and unsupervised learning) as they are more promising for future use and devoid of any of the weaknesses of rule-based systems.…”

Section: Related Workmentioning

confidence: 99%

Machine-Learning-Based Scoring System for Antifraud CISIRTs in Banking Environment

et al. 2023

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The number of fraud occurrences in electronic banking is rising each year. Experts in the field of cybercrime are continuously monitoring and verifying network infrastructure and transaction systems. Dedicated threat response teams (CSIRTs) are used by organizations to ensure security and stop cyber attacks. Financial institutions are well aware of this and have increased funding for CSIRTs and antifraud software. If the company has a rule-based antifraud system, the CSIRT can examine fraud cases and create rules to counter the threat. If not, they can attempt to analyze Internet traffic down to the packet level and look for anomalies before adding network rules to proxy or firewall servers to mitigate the threat. However, this does not always solve the issues, because transactions occasionally receive a “gray” rating. Nevertheless, the bank is unable to approve every gray transaction because the number of call center employees is insufficient to make this possible. In this study, we designed a machine-learning-based rating system that provides early warnings against financial fraud. We present the system architecture together with the new ML-based scoring extension, which examines customer logins from the banking transaction system. The suggested method enhances the organization’s rule-based fraud prevention system. Because they occur immediately after the client identification and authorization process, the system can quickly identify gray operations. The suggested method reduces the amount of successful fraud and improves call center queue administration.

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“…Several sectors have been working on cyber security with different perspectives. In [22], researchers focused on a significant obstacle in the financial industry, namely the need for real-time cybersecurity analytics on financial transaction data. Their solution presents a novel approach to combining supervised and unsupervised artificial intelligence models, leveraging appropriate technological tools capable of efficiently processing vast quantities of transaction data.…”

Section: Cybersecurity Applications With Lambda Architecturementioning

confidence: 99%

“…This is accomplished by employing batch processing techniques to generate comprehensive and precise representations of batch data. Simultaneously, real-time stream processing is utilized to generate representations of online data in a timely manner [22]. Another research [3] presents a novel Network Traffic Analyzer, which serves as a vital element within the cyber threat intelligence information sharing architecture (CTI2SA) of the Cyber-pi project.…”

Section: Cybersecurity Applications With Lambda Architecturementioning

confidence: 99%

Lambda Architecture-Based Big Data System for Large-Scale Targeted Social Engineering Email Detection

DEMİREZEN,

SELCEN NAVRUZ

2023

International Journal of Information Security Science

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In this research, we delve deep into the realm of Targeted Social Engineering Email Detection, presenting a novel approach that harnesses the power of Lambda Architecture (LA). Our innovative methodology strategically segments the BERT model into two distinct components: the embedding generator and the classification segment. This segmentation not only optimizes resource consumption but also improves system efficiency, making it a pioneering step in the field. Our empirical findings, derived from a rigorous comparison between the fastText and BERT models, underscore the superior performance of the latter. Specifically, The BERT model has high precision rates for identifying malicious and benign emails, with impressive recall values and F1 scores. Its overall accuracy rate was 0.9988, with a Matthews Correlation Coefficient value of 0.9978. In comparison, the fastText model showed lower precision rates. Leveraging principles reminiscent of the Lambda architecture, our study delves into the performance dynamics of data processing models. The Separated-BERT (Sep-BERT) model emerges as a robust contender, adept at managing both real-time (stream) and large-scale (batch) data processing. Compared to the traditional BERT, Sep-BERT showcased superior efficiency, with reduced memory and CPU consumption across diverse email sizes and ingestion rates. This efficiency, combined with rapid inference times, positions Sep-BERT as a scalable and cost-effective solution, aligning well with the demands of Lambda- inspired architectures. This study marks a significant step forward in the fields of big data and cybersecurity. By introducing a novel methodology and demonstrating its efficacy in detecting targeted social engineering emails, we not only advance the state of knowledge in these domains but also lay a robust foundation for future research endeavors, emphasizing the transformative potential of integrating advanced big data frameworks with machine learning models.

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Cybersecurity and Fraud Detection in Financial Transactions

Cited by 9 publications

References 8 publications

End-to-End Real-time Architecture for Fraud Detection in Online Digital Transactions

End-to-End Real-time Architecture for Fraud Detection in Online Digital Transactions

Machine-Learning-Based Scoring System for Antifraud CISIRTs in Banking Environment

Lambda Architecture-Based Big Data System for Large-Scale Targeted Social Engineering Email Detection

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