Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve

Enhancing credit card fraud detection: an ensemble machine learning approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve

Integrated Security And Privacy Preservation Approach In Mobile Crowd Sensing

The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features introduces a novel sensing paradigm called mobile crowd sensing (MCS). Despite its opportunities and advantages, mobile crowd sensing still faces security and privacy issues. One major issue with effectively protecting sensitive data (such as location information) of users in MCS is the inability to annotate raw readings from smartphone GPS sensor due to its inherent “on” and “off” state. Another problem is detecting malicious data at the sensing stage of MCS. A preferred method to solve the identified problem without affecting the user experience is to annotate “sensitive”, “non-sensitive” and “malicious” data automaticall

Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors

Automatic data annotation eliminates most of the challenges we faced due to the manual methods of annotating sensor data. It significantly improves users’ experience during sensing activities since their active involvement in the labeling process is reduced. An unsupervised learning technique such as clustering can be used to automatically annotate sensor data. However, the lingering issue with clustering is the validation of generated clusters. In this paper, we adopted the k-means clustering algorithm for annotating unlabeled sensor data for the purpose of detecting sensitive location information of mobile crowd sensing users. Furthermore, we proposed a cluster validation index for the k-means algorithm, which is based on Multiple Pair-Frequency. Thereafter, we trained three classifiers (Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes) using cluster labels generated from the k-means clustering algorithm. The accuracy, precision, and recall of these classifiers were evaluated during the classification of “non-sensitive” and “sensitive” data from motion and location sensors. Very high accuracy scores were recorded from Support Vector Machine and K-Nearest Neighbor classifiers while a fairly high accuracy score was recorded from the Naïve Bayes classifier. With the hybridized machine learning (unsupervised and supervised) technique presented in this paper, unlabeled sensor data was automatically annotated and then classified

SenseCrypt: A Security Framework for Mobile Crowd Sensing Applications

The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the “on” and “off” state of global positioning system sensor in smartphones. To address this problem, this paper proposes “SenseCrypt”, a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework

Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors

Automatic data annotation eliminates most of the challenges we faced due to the manual methods of annotating sensor data. It significantly improves users’ experience during sensing activities since their active involvement in the labeling process is reduced. An unsupervised learning technique such as clustering can be used to automatically annotate sensor data. However, the lingering issue with clustering is the validation of generated clusters. In this paper, we adopted the k-means clustering algorithm for annotating unlabeled sensor data for the purpose of detecting sensitive location information of mobile crowd sensing users. Furthermore, we proposed a cluster validation index for the k-means algorithm, which is based on Multiple Pair-Frequency. Thereafter, we trained three classifiers (Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes) using cluster labels generated from the k-means clustering algorithm. The accuracy, precision, and recall of these classifiers were evaluated during the classification of “non-sensitive” and “sensitive” data from motion and location sensors. Very high accuracy scores were recorded from Support Vector Machine and K-Nearest Neighbor classifiers while a fairly high accuracy score was recorded from the Naïve Bayes classifier. With the hybridized machine learning (unsupervised and supervised) technique presented in this paper, unlabeled sensor data was automatically annotated and then classified