Comparative analysis of credit card fraud detection in Simulated Annealing trained Artificial Neural Network and Hierarchical Temporal Memory

Osegi, Emmanuel N.; Jumbo, E.F.

doi:10.1016/j.mlwa.2021.100080

Cited by 23 publications

(9 citation statements)

References 8 publications

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“…Depending on the amount of their internal memory. RNN applied in seven articles in this review ( Bandyopadhyay & Dutta, 2020 ; Chen & Lai, 2021 ; Forough & Momtazi, 2021 ; Hussein et al, 2021 ; Osegi & Jumbo, 2021 ; Sadgali, Sael & Benabbou, 2021 ; Zhang et al, 2021 ). In Bandyopadhyay & Dutta (2020) , Implementing and applying RNN on synthetic dataset.…”

Section: Results and Analysismentioning

confidence: 99%

“…LSTM architecture enables sequence prediction problems to be learned through long-term reliance. LSTM and BiLSTM applied in eight articles ( Agarwal et al, 2021 ; Alghofaili, Albattah & Rassam, 2020 ; Benchaji, Douzi & El Ouahidi, 2021 ; Cheon et al, 2021 ; Forough & Momtazi, 2021 ; Nguyen et al, 2020 ; Osegi & Jumbo, 2021 ; Sadgali, Sael & Benabbou, 2021 ). In Alghofaili, Albattah & Rassam (2020) , a new model developed to improve both the present detection techniques and the detection accuracy in light of huge data.…”

Section: Results and Analysismentioning

confidence: 99%

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A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush

Zhou

Gururajan

et al. 2023

PeerJ Computer Science

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The increasing spread of cyberattacks and crimes makes cyber security a top priority in the banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional anomaly detection and rule-based techniques are two of the most common utilized approaches for detecting cyber fraud, however, they are the most time-consuming, resource-intensive, and inaccurate. Machine learning is one of the techniques gaining popularity and playing a significant role in this field. This study examines and synthesizes previous studies on the credit card cyber fraud detection. This review focuses specifically on exploring machine learning/deep learning approaches. In our review, we identified 181 research articles, published from 2019 to 2021. For the benefit of researchers, review of machine learning/deep learning techniques and their relevance in credit card cyber fraud detection is presented. Our review provides direction for choosing the most suitable techniques. This review also discusses the major problems, gaps, and limits in detecting cyber fraud in credit card and recommend research directions for the future. This comprehensive review enables researchers and banking industry to conduct innovation projects for cyber fraud detection.

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush

Zhou

Gururajan

et al. 2023

PeerJ Computer Science

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“…Chen et al [15] proposed a Text Convolutional Neural Network (TextCNN) based method combined with Transformer for detecting Ponzi SCs, but this method may cause partial information loss due to decompiling bytecode into Solidity source code. In addition, Osegi et al [16] proposed an Artificial Neural Network trained by the Simulated Annealing technique (SA-ANN) and a Hierarchical Temporal Memory based on the Cortical Learning Algorithms (HTM-CLA) have been proposed to detect credit card fraud (CCF). The HTM-CLA was found to outperform the SA-ANN and the Long Short-Term Memory ANN (LSTM-ANN) by a factor of 2:1.…”

Section: Research On Fraud Smart Contractsmentioning

confidence: 99%

The Detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement

Zhou,

Yang,

Wang

et al. 2023

CMC

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With the increasing popularity of Ethereum, smart contracts have become a prime target for fraudulent activities such as Ponzi, honeypot, gambling, and phishing schemes. While some researchers have studied intelligent fraud detection, most research has focused on identifying Ponzi contracts, with little attention given to detecting and preventing gambling or phishing contracts. There are three main issues with current research. Firstly, there exists a severe data imbalance between fraudulent and non-fraudulent contracts. Secondly, the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent contracts. Lastly, most prior studies have used contract source code as raw features, but many smart contracts only exist in bytecode.To address these issues, we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet (ECA-EfficientNet) and data enhancement. Our method begins by converting bytecode into Red Green Blue (RGB) three-channel images and then applying channel exchange data enhancement. We then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB images. Our proposed method achieves high F1score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi, honeypot, gambling, and phishing smart contracts. The results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract detection. Our research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts.

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“…Cluster analysis is an attempt to identify groups of similar objects and help find distribution patterns and relationship patterns in large data sets [16]. The important thing in the clustering process is declaring a set of patterns to the appropriate group that is useful for finding similarities and differences so that it can produce valuable conclusions [17].…”

Section: Single Linkagementioning

confidence: 99%

Implementation of Single Linked on Machine Learning for Clustering Student Scientific Fields

Arif,

Dahria,

Defit

et al. 2022

matrik

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Machine Learning in classifying scientific fields according to the competence of students. Currently STMIK Triguna Dharma is quite difficult to map the scientific fields that will be used by students in submitting titles, so that the results of the thesis made are less than optimal. For this reason, it is necessary to map this concentration to assist students in completing theses through specialization classes. The Mechanical Learning technique used in solving this problem is to use the Single Linkage Technique. The process of testing the method begins with determining the standard data used and then looking for the proximity value using Euclidean so that later cluster results will be obtained from mapping scientific fields. From the Single Linkage Technique process that has been carried out, several cluster results will be obtained, namely clusters that map groups of STMIK Triguna Dharma students who have competence and clusters that map groups of STMIK Triguna Dharma students who lack competence. From the results of this grouping, the institution will make specialization classes according to the resulting cluster. Thus creating a specialization class that is in accordance with the competencies possessed by STMIK Triguna Dharma students

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Comparative analysis of credit card fraud detection in Simulated Annealing trained Artificial Neural Network and Hierarchical Temporal Memory

Cited by 23 publications

References 8 publications

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

The Detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement

Implementation of Single Linked on Machine Learning for Clustering Student Scientific Fields

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