Autonomous Artificial Intelligence Systems for Fraud Detection and Forensics in Dark Web Environments

RAWAT, Romil; Oki, Olukayode; Chakrawarti, Rajesh Kumar; Adekunle, Temitope Samson; Gonzáles, José Luis Arias; Ajagbe, Sunday Adeola

doi:10.31449/inf.v46i9.4538

Cited by 11 publications

(1 citation statement)

References 44 publications

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“…Scholars reveal a variety of cyber fraud techniques targeting banks, including phishing (Yoro et al, 2023), malware attacks (Shamsi et al, 2023), identity and credit card theft (Dewi et al, 2023;Kuzior & Kuzior, 2018), and apps -extortionists (Rawat et al, 2023). Financial loss, reputational damage and the erosion of trust are common outcomes of cyber fraud.…”

Section: Literature Reviewmentioning

confidence: 99%

The Modeling of the Probable Behaviour of Insider Cyber Fraudsters in Banks

Yarovenko,

Kuzior,

Raputa

2023

FMIR

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Insider cyber fraud in the banking sector is a serious and complex issue for financial institutions. This form of cyber fraud is particularly insidious due to insiders’ inherent access and knowledge, necessitating banks to implement comprehensive strategies for detecting, preventing, and responding to these internal threats. The aim of this study is to develop a scientific and methodological approach to model the probable behaviour of insider cyber fraudsters in banks based on a complex combination of principal component analysis, k-means clustering, and associative analysis. During the analysis of current challenges in the financial sector regarding the evolution of cyber fraud and its implications, the systematization of existing theoretical approaches concerning the examination of cyber fraud in banks was performed. Its result revealed a positive trend in the dynamics of the number of published materials in conferences and articles using keywords “cyber” and “frauds” in the Scopus database from 2000 to 2023. Additionally, utilizing the VOSviewer software facilitated the systematization of keyword combinations used in scholarly publications on the chosen topic, forming clusters to visualize and organize vectors of scientific research. Analytical data from Google Trends on critical issues related to cyber fraud were chosen as input data. Twenty variables were formed, which are the results of search queries, characterizing cyberattacks and decreased trust in financial institutions. The principal components method was used to reduce the dimensionality of the input data array, making it possible to select the nine most significant for the study. Conducting a cluster analysis using the k-means method made it possible to form 3 main groups of search queries, which included 12 of the selected variables. The results of the performed procedures contributed to the implementation of associative analysis for three sets of variables. It has been found that what intrigues potential insider cybercriminals in banks the most is the personal financial information of the client, access to the client’s profile in online banking and gaining access to his phone data. The obtained results can be utilized by commercial banks for identifying potential insider cyber fraudsters and ensuring a higher level of client protection against the actions of insider cyber fraudsters, by bank clients for analysing and mitigating potential threats from insider cyber fraudsters, and by law enforcement agencies for prompt responses to potential threats posed by insider cyber fraudsters in banks.

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Section: Literature Reviewmentioning

confidence: 99%

The Modeling of the Probable Behaviour of Insider Cyber Fraudsters in Banks

Yarovenko,

Kuzior,

Raputa

2023

FMIR

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Self-Adaptive Incremental PCA-Based DBSCAN of Acoustic Features for Anomalous Sound Detection

Tan,

Yiu

2024

SN COMPUT. SCI.

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In modern industry, maintaining continuous machine operations is important for improving production efficiency and reducing costs. Therefore, the smart technology of acoustic monitoring to detect anomalous machine conditions earlier before breakdowns works as part of predictive maintenance and is applied not only in industry fault detection but also in safety monitoring and surveillance systems. This paper proposes a self-adaptive unsupervised machine learning algorithm with dimension-reduction technology to detect anomalous sounds after extracting acoustic machine features. Technically, the automatic EPS calculation algorithm-based genetic algorithm optimizes the automatic clustering algorithm’s configuration for incremental principal component analysis and density-based spatial clustering algorithms with noise. IPCA is enhanced by the sequential Karhunen–Loeve (SKL) algorithm, and the condensation algorithm works as the second layer of the algorithm to reduce the number of effective components. This architecture could select an optimized set of parameters based on different test environments and keeps performance quality with fewer computational requirements. In the experiments, 228 sets of normal sounds and 100 sets of anomaly sounds are used. The sound files are collected from the same machine type (stepper motors) at a real plant site. We compare the proposed algorithm with K-means++, one-class SVM, agglomerative clustering, DCGAN and DCNN-Autoencoder, and this new algorithm performs best, with an AUC of 0.84 and the shortest execution time. The algorithm is generic and can be applied to detect anomalies in machines to provide early warning to people to avoid serious accidents or disasters.

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