A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

Alharbi, Abdullah; Alshammari, Majid; Okon, Ofonime Dominic; Alabrah, Amerah; Rauf, Hafiz Tayyab; Alyami, Hashem; Meraj, Talha

doi:10.3390/electronics11050756

Cited by 48 publications

(16 citation statements)

References 48 publications

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“…Machine-learning-based schemes and methods have been extensively adopted in different real-time problems. These problems include energy consumption minimization [21], object detection [22], and more specifically the information security domain problems such as credit card fraud detection [23], CAPTCHA solving [24] to enhance the security of CAPTCHA-based security questions. Few of the recent studies focused on feature selection and class imbalance problems while proposing an IDPS.…”

Section: Related Workmentioning

confidence: 99%

An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method

Alabrah

2023

Applied Sciences

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Network security problems arise these days due to many challenges in cyberspace. The malicious attacks on installed wide networks are rapidly spreading due to their vulnerability. Therefore, the user and system information are at high risk due to network attacks. To protect networks against these attacks, Network Intrusion Detection and Prevention Systems (NIDPS) are installed on them. These NIDPS can detect malicious attacks by monitoring abnormal behavior and patterns in network traffic. These systems were mainly developed using Artificial Intelligence (AI) algorithms. These intelligent NIDPS are also able to detect the attack type while detecting network attacks. Previous studies have proposed many NIDPS for network security. However, many challenges exist so far such as limited available data for training AI algorithms, class imbalance problems, and automated selection of the most important features. These problems need to be solved first, which will lead to the precise detection of network attacks. Therefore, the proposed framework used the highly imbalanced UNSW-NB15 dataset for binary and multiclass classification of network attacks. In this framework, firstly dataset normalization is applied using standard deviation and the mean of feature columns; secondly, an Improved Salp Swarm Algorithm (ISSA) is applied for automated feature selection separately on binary and multiclass subsets. Thirdly, after applying feature selection, the SMOTE–Tomek class balancing method is applied where at least four different ML classifiers are used for binary and multiclass classification. The achieved results outperformed as compared to previous studies and improved the overall performance of NIDPS.

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Section: Related Workmentioning

confidence: 99%

An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method

Alabrah

2023

Applied Sciences

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“…This technique obtained better performance with minimum prediction time, but the memory utilization was high. Abdullah Alharbi et al 35 established a model on the basis of deep learning (DL) and the experimentation was done using Kaggle data. Here, the conversion approach, called text2IMG was used in the generation of small images.…”

Section: Literature Surveymentioning

confidence: 99%

Shuffled shepherd political optimization‐based deep learning method for credit card fraud detection

Ganji

Chaparala²,

Sajja³

2023

Concurrency and Computation

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Summary Digital transactions based on credit cards are gradually increasing concept due to expediency. The amount of fraudulent transactions has intensely enlarged in modern days, because of the fast development of e‐services, namely e‐finance, mobile payments, and e‐commerce as well as the promotion of credit cards. Criminal fraud behaviors and user's payment behaviors are frequently varying, thus performance improvement of the fraud identification method and its stability are more challenging processes. The Shuffled Shepherd Political Optimization‐based Deep Residual network (SSPO‐based DRN) scheme is established for credit card fraud identification in this research. The SSPO is developed by merging the Political Optimization (PO) and Shuffled Shepherd Optimization Algorithm (SSOA). The quantile normalization model is an effective preprocessing technique, which normalizes the data for effective detection. Moreover, fisher score and class information gain effectively select the required features. Data augmentation is employed for increasing the data size, thereby the detection performance is improved. The Deep Residual Network (DRN) is employed for credit card fraud recognition, which is trained by devised SSPO algorithm. The SSPO‐based DRN approach achieved enhanced performance with testing sensitivity of 0.9279, specificity of 0.9023, and accuracy of 0.9120.

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“…Since actual oil production is the primary focus of this model, it must be explicitly accounted for in regulatory frameworks to guarantee that the fiscal policy conveys a shock to the economy, as predicted by the model. When calculating losses due to deviations from an acyclical fiscal policy, the zero oil production answer for public expenditure is used as a baseline ( Alharbi et al, 2022 ). Therefore, a pro- or mitigation fiscal posture equates to a positive or output dc reaction.…”

Section: Introductionmentioning

confidence: 99%