Advanced Payment Security System:XGBoost, CatBoost and SMOTE Integrated
- URL: http://arxiv.org/abs/2406.04658v1
- Date: Fri, 7 Jun 2024 05:56:43 GMT
- Title: Advanced Payment Security System:XGBoost, CatBoost 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 XGBoost and LightGBM, for developing a more accurate and robust Payment Security Protection Model.
The results show that these models not only outperform traditional approaches but also hold significant promise for advancing the field of transaction fraud prevention.
- 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 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 used 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. Key metrics such as Precision, Recall, and F1 Score were used to rigorously assess 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. The results show that these models not only outperform traditional approaches but also hold significant promise for advancing the field of transaction fraud prevention.
Related papers
- 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) - Leveraging LSTM and GAN for Modern Malware Detection [0.4799822253865054]
This paper proposes the utilization of the Deep Learning Model, LSTM networks, and GAN classifiers to amplify malware detection accuracy and speed.
The research outcomes come out with 98% accuracy that shows the efficiency of deep learning plays a decisive role in proactive cybersecurity defense.
arXiv Detail & Related papers (2024-05-07T14:57:24Z) - Enhancing Security in Federated Learning through Adaptive
Consensus-Based Model Update Validation [2.28438857884398]
This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks.
We propose a consensus-based verification process integrated with an adaptive thresholding mechanism.
Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience.
arXiv Detail & Related papers (2024-03-05T20:54:56Z) - An Adversarial Robustness Benchmark for Enterprise Network Intrusion
Detection [0.0]
The robustness of regularly and adversarially trained RF, XGB, LGBM, and EBM models was evaluated.
NewCICIDS led to models with a better performance, especially XGB and EBM, but RF and LGBM were less robust against the more recent cyber-attacks of HIKARI.
arXiv Detail & Related papers (2024-02-25T16:45:39Z) - Designing an attack-defense game: how to increase robustness of
financial transaction models via a competition [69.08339915577206]
Given the escalating risks of malicious attacks in the finance sector, understanding adversarial strategies and robust defense mechanisms for machine learning models is critical.
We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input.
We have designed a competition that allows realistic and detailed investigation of problems in modern financial transaction data.
The participants compete directly against each other, so possible attacks and defenses are examined in close-to-real-life conditions.
arXiv Detail & Related papers (2023-08-22T12:53:09Z) - Avoid Adversarial Adaption in Federated Learning by Multi-Metric
Investigations [55.2480439325792]
Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources.
FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks.
We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously.
MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
arXiv Detail & Related papers (2023-06-06T11:44:42Z) - Publishing Efficient On-device Models Increases Adversarial
Vulnerability [58.6975494957865]
In this paper, we study the security considerations of publishing on-device variants of large-scale models.
We first show that an adversary can exploit on-device models to make attacking the large models easier.
We then show that the vulnerability increases as the similarity between a full-scale and its efficient model increase.
arXiv Detail & Related papers (2022-12-28T05:05:58Z) - 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) - SafeAMC: Adversarial training for robust modulation recognition models [53.391095789289736]
In communication systems, there are many tasks, like modulation recognition, which rely on Deep Neural Networks (DNNs) models.
These models have been shown to be susceptible to adversarial perturbations, namely imperceptible additive noise crafted to induce misclassification.
We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition models.
arXiv Detail & Related papers (2021-05-28T11:29:04Z) - 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)
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.