Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integrated

• URL: http://arxiv.org/abs/2406.04658v2
• Date: Fri, 26 Jul 2024 05:07:22 GMT
• Title: Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integrated
• Authors: Qi Zheng, Chang Yu, Jin Cao, Yongshun Xu, Qianwen Xing, Yinxin Jin,
• Abstract summary: This study explores the application of advanced machine learning models, specifically based on XGBoost and LightGBM. By selecting highly correlated features, we aimed to strengthen the training process and boost model performance. Our detailed analyses and comparisons reveal that the combination of SMOTE with XGBoost and LightGBM offers a highly efficient and powerful mechanism for payment security protection.
• Score: 16.906931748453342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rise of various online and mobile payment systems, transaction fraud has become a significant threat to financial security. This study explores the application of advanced machine learning models, specifically based on XGBoost and LightGBM, for developing a more accurate and robust Payment Security Protection Model. To enhance data reliability, we meticulously processed the data sources and applied SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance and improve data representation. By selecting highly correlated features, we aimed to strengthen the training process and boost model performance. We conducted thorough performance evaluations of our proposed models, comparing them against traditional methods including Random Forest, Neural Network, and Logistic Regression. Using metrics such as Precision, Recall, and F1 Score, we rigorously assessed their effectiveness. Our detailed analyses and comparisons reveal that the combination of SMOTE with XGBoost and LightGBM offers a highly efficient and powerful mechanism for payment security protection. Moreover, the integration of XGBoost and LightGBM in a Local Ensemble model further demonstrated outstanding performance. After incorporating SMOTE, the new combined model achieved a significant improvement of nearly 6\% over traditional models and around 5\% over its sub-models, showcasing remarkable results.

Related papers

• A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models [9.304845676825584]
  We propose a novel adversarial training framework that integrates multiple attack strategies and advanced machine learning techniques. Experiments conducted on real-world datasets, including CIFAR-10 and CIFAR-100, demonstrate that the proposed method significantly enhances model robustness.
  arXiv  Detail & Related papers  (2024-10-18T23:47:46Z)
• UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation [93.38604803625294]
  We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) We use Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. UncertaintyRAG outperforms baselines by 2.03% on LLaMA-2-7B, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-03T17:39:38Z)
• Enhanced Credit Score Prediction Using Ensemble Deep Learning Model [12.85570952381681]
  This paper combines high-performance models like XGBoost and LightGBM, already widely used in modern banking systems, with the powerful TabNet model. We have developed a potent model capable of accurately determining credit score levels by integrating Random Forest, XGBoost, and TabNet, and through the stacking technique in ensemble modeling.
  arXiv  Detail & Related papers  (2024-09-30T21:56:16Z)
• Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks [3.1309870454820277]
  This paper introduces a hybrid Intrusion Detection System (IDS) that combines Kolmogorov-Arnold Networks (KANs) with the XGBoost algorithm. Our proposed IDS leverages the unique capabilities of KANs, which utilize learnable activation functions to model complex relationships within data, alongside the powerful ensemble learning techniques of XGBoost. Experimental evaluations demonstrate that our hybrid IDS achieves an impressive detection accuracy exceeding 99% in distinguishing between benign and malicious activities.
  arXiv  Detail & Related papers  (2024-08-28T15:58:49Z)
• Credit Card Fraud Detection Using Advanced Transformer Model [15.34892016767672]
  This study focuses on innovative applications of the latest Transformer models for more robust and precise fraud detection. We meticulously processed the data sources, balancing the dataset to address the issue of data sparsity significantly. We conducted performance comparisons with several widely adopted models, including Support Vector Machine (SVM), Random Forest, Neural Network, and Logistic Regression.
  arXiv  Detail & Related papers  (2024-06-06T04:12:57Z)
• CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration [59.48235003469116]
  We show that data augmentation consistently enhances OOD performance. We also show that CF augmented models which are easier to calibrate also exhibit much lower entropy when assigning importance.
  arXiv  Detail & Related papers  (2023-09-14T16:16:40Z)
• CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships [8.679073301435265]
  We construct a new benchmark for evaluating and improving model robustness by applying perturbations to existing data. We use these labels to perturb the data by deleting non-causal agents from the scene. Under non-causal perturbations, we observe a $25$-$38%$ relative change in minADE as compared to the original.
  arXiv  Detail & Related papers  (2022-07-07T21:28:23Z)
• Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines [65.0803400763215]
  This work critically examines how adversarial robustness guarantees change when state-of-the-art certifiably robust models encounter out-of-distribution data. We propose a novel data augmentation scheme, FourierMix, that produces augmentations to improve the spectral coverage of the training data. We find that FourierMix augmentations help eliminate the spectral bias of certifiably robust models enabling them to achieve significantly better robustness guarantees on a range of OOD benchmarks.
  arXiv  Detail & Related papers  (2021-12-01T17:11:22Z)
• Sparse MoEs meet Efficient Ensembles [49.313497379189315]
  We study the interplay of two popular classes of such models: ensembles of neural networks and sparse mixture of experts (sparse MoEs) We present Efficient Ensemble of Experts (E$3$), a scalable and simple ensemble of sparse MoEs that takes the best of both classes of models, while using up to 45% fewer FLOPs than a deep ensemble.
  arXiv  Detail & Related papers  (2021-10-07T11:58:35Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• Cauchy-Schwarz Regularized Autoencoder [68.80569889599434]
  Variational autoencoders (VAE) are a powerful and widely-used class of generative models. We introduce a new constrained objective based on the Cauchy-Schwarz divergence, which can be computed analytically for GMMs. Our objective improves upon variational auto-encoding models in density estimation, unsupervised clustering, semi-supervised learning, and face analysis.
  arXiv  Detail & Related papers  (2021-01-06T17:36:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.