An Innovative Sensing Machine Learning Technique to Detect Credit Card Frauds in Wireless Communications

Sasikala, G.; Mohan, Laavanya; Sathyasri, B.; Supraja, C.; Mahalakshmi, V.; Mole, S. S. Sreeja; Mulerikkal, Jaison; Chidambaranathan, S.; Chakrapani, Arvind; Srihari, K.; Dejene, Minilu

doi:10.1155/2022/2439205

Cited by 10 publications

(10 citation statements)

References 33 publications

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“…DNN in its bid to learn, and adapt useful selected parameters via a carefully constructed deep, multilayer net that aims to improve forecast precision. Its hidden layer often transforms [51] data non-linearly and passes it on from a previous layer to its next [52]. With learning handed over to the DNN, [53] stressed that the DNN trains itself using 2-stages namely pretraining, and fine-tuning.…”

Section: Experimental Delcluste Ensemblementioning

confidence: 99%

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DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

Aghware,

Yoro,

Ejeh

et al. 2023

IJACSA

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Fraud is the unlawful acquisition of valuable assets gained via intended misrepresentation. It is a crime committed by either an internal/external user, and associated with acts of theft, embezzlement, and larceny. The proliferation of credit cards to aid financial inclusiveness has its usefulness alongside it attracting malicious attacks for gains. Attempts to classify fraudulent credit card transactions have yielded formal taxonomies as these attacks seek to evade detection. We propose a deep learning ensemble via a profile hidden Markov model with a deep neural network, which is poised to effectively classify credit-card fraud with a high degree of accuracy, reduce errors, and timely fashion. The result shows the ensemble effectively classified benign transactions with a precision of 97 percent. Thus, we posit a new scheme that is more logical, intuitive, reusable, exhaustive, and robust in classifying such fraudulent transactions based on the attack source, cause(s), and attack time gap.

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Section: Experimental Delcluste Ensemblementioning

confidence: 99%

“…The stages are as thus [14], [52], [53], [60]- [62]: And each sub-DNN is trained, and labelled from 1 to k [64], [65].…”

Section: Experimental Delcluste Ensemblementioning

confidence: 99%

DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

Aghware,

Yoro,

Ejeh

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Typically, the confusion matrix is used to illustrate that a machine learning model's prediction does not correspond to the dataset's underlying truth. The confusion matrix has the following items: [5] True positive, False positive (FP), and False negative (FN). In light of this, we will estimate the model using the Recall, Precision, Accuracy, and F1-score of the confusion matrix score.…”

Section: Confusion Matrixmentioning

confidence: 99%

“…The goal of ML, a subset of computer science and artificial intelligence, is to accurately imitate human learning through the use of data and algorithms. [5] This suggests that it makes it possible for computers to gain knowledge from the past in order to make more accurate forecasts. [6] By learning from previous datasets, ML may be utilized to adjust to the unpredictable and covert nature of credit card fraud.…”

Section: Introductionmentioning

confidence: 99%

Assessing the feasibility of machine learning-based modelling and prediction of credit fraud outcomes using hyperparameter tuning

2023

ACSS

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Both the actual theft of a credit card and the deletion of private credit card data are considered forms of credit card fraud. For detection, there are numerous machine learning algorithms accessible. So, several algorithms that can be used to categorize transactions as fraudulent or lawful are illustrated in this study. In this experiment, the credit card fraud prediction dataset was utilized. The dataset is extremely skewed, hence undersampling is used rather than oversampling. The dataset is separated into test and training data portions, and feature selection is made. The experiment uses the methods of Logistic Regression, Random Forest, SVM, ADABoost, XGBoost, and LightGBM. Moreover, the SMOTE and Optuna's hyperparameter tweaking ways provide model customization. The findings suggest that specific algorithms may be capable of accurately recognizing credit card fraud.

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“…Traditional collection, storage, and processing of electronic health records utilize centralized techniques that pose several risks and lean systems toward a number of data breaches and attacks that compromise data availability [13]- [15]. The blockchain is gradually resolving these challenges with its immutability feat that prevents records alteration.…”

Section: Introductionmentioning

confidence: 99%

BEHeDaS: A Blockchain Electronic Health Data System for Secure Medical Records Exchange

Oladele,

Ojugo,

Odiakaose

et al. 2024

J. Comput. Theor. Appl.

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Blockchain platforms propagate into every facet, including managing medical services with professional and patient-centered applications. With its sensitive nature, record privacy has become imminent with medical services for patient diagnosis and treatments. The nature of medical records has continued to necessitate their availability, reachability, accessibility, security, mobility, and confidentiality. Challenges to these include authorized transfer of patient records on referral, security across platforms, content diversity, platform interoperability, etc. These, are today – demystified with blockchain-based apps, which proffers platform/application services to achieve data features associated with the nature of the records. We use a permissioned-blockchain for healthcare record management. Our choice of permission mode with a hyper-fabric ledger that uses a world-state on a peer-to-peer chain – is that its smart contracts do not require a complex algorithm to yield controlled transparency for users. Its actors include patients, practitioners, and health-related officers as users to create, retrieve, and store patient medical records and aid interoperability. With a population of 500, the system yields a transaction (query and https) response time of 0.56 seconds and 0.42 seconds, respectively. To cater to platform scalability and accessibility, the system yielded 0.78 seconds and 063 seconds, respectively, for 2500 users.

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An Innovative Sensing Machine Learning Technique to Detect Credit Card Frauds in Wireless Communications

Cited by 10 publications

References 33 publications

DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

Assessing the feasibility of machine learning-based modelling and prediction of credit fraud outcomes using hyperparameter tuning

BEHeDaS: A Blockchain Electronic Health Data System for Secure Medical Records Exchange

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