Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.

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Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques

Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine learning based fraudulent transaction detection systems are very effective in real-world scenarios. However, ...

Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real time. Based on a weight agnostic neural networks (WANNs) methodology, we propose an automated searching neural net architecture strategy that can perform various tasks such as identifying zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

https://www.mdpi.com/2079-9292/10/7/781/pdf

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

Credit Card Fraud (CCF) is a serious challenge facing credit card holder and the credit card delivering companies in the past decades. There are two levels CCF are performed, the transaction level frauds and application level frauds. This paper focuses on the application level of CCF detection using Genetic Algorithm (GA) as a feature selection technique. The GA feature selection technique is in two phases, the first phase is designated as the first priority features where eight (8) attributes were selected as the fittest attributes. At second stage which is referred to as the second priority features where another set of eight (8) attributes were considered and selected. The Naive Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM) supervised machine learning techniques were used for the detection of CCF on German credit card dataset which is an imbalance dataset. The experimental findings of the proposed model revealed that the first priority features are the most important features. Also, the obtained results showed that the RF algorithm outperformed NB and SVM in terms of accuracy, fraud detection rate and precision.

Application of GA Feature Selection on Naive Bayes, Random Forest and SVM for Credit Card Fraud Detection

Machine Learning-enabled Optimization of Extrusion-based 3D Printing.

Credit card fraud detection is presently the most frequently occurring problem in the present world. This is due to the rise in both online transactions and e-commerce platforms. Credit card fraud generally happens when the card was stolen for any of the unauthorized purposes or even when the fraudster uses the credit card information for his use. In the present world, we are facing a lot of credit card problems. To detect the fraudulent activities the credit card fraud detection system was introduced. This project aims to focus mainly on machine learning algorithms. The algorithms used are random forest algorithm and the Adaboost algorithm. The results of the two algorithms are based on accuracy, precision, recall, and F1-score. The ROC curve is plotted based on the confusion matrix. The Random Forest and the Adaboost algorithms are compared and the algorithm that has the greatest accuracy, precision, recall, and F1-score is considered as the best algorithm that is used to detect the fraud.

Credit Card Fraud Detection Using Machine Learning

This paper describes the step by step procedure to make SSL strip attack any secured https website Though we are having the SSL certificate for a website, we are subjected to MITM attacks, from this; we can say that SSL provides a false sense of security Both HTTP and HTTPS are the application layer protocols in the TCP/IP model Using HTTP communication protocol, the data which is being transmitted is in a decrypted format that is in the form of plain text which, when sniffed by the attacker, is like an open book which is not at all safe and completely useless while using complicated websites like online banking So https is used where the data is transmitted in a secure tunnel, which is nothing but the link established between the web server and the browser, and the information which is sent between them is encrypted, which when sniffed by an attacker is of no use to him So, we can say that https is secure until the's' is stripped from https, which is known as an SSL strip attack SSL strip attack is downgrading the https site to HTTP by using various methods Here we are using ARP spoofing to strip HTTPS to HTTP

G. Ramakoteswara Rao

Papers

Credit Card Fraud Detection Using Machine Learning

Secure Socket Layer Stripping Attack Using Address Resolution Protocol Spoofing