Research

16 pages, 2019 KiB

Open AccessArticle

Signed Fingermark Liveness Detection Method Based on Deep Residual Networks and Multimodal Decision Fusion

by Yongliang Zhang, Zihan Zhou, Jiahang Wang and Zipeng Chen

Appl. Sci. 2024, 14(5), 1998; https://doi.org/10.3390/app14051998 - 28 Feb 2024

Viewed by 419

Fingermarks play an important role in document identification. At the same time, fingermarks on paper documents are often accompanied by signatures and background text, which introduce noise to the original fingermark textures and increase the difficulty of detection. A signed fingermark detection method [...] Read more.

Fingermarks play an important role in document identification. At the same time, fingermarks on paper documents are often accompanied by signatures and background text, which introduce noise to the original fingermark textures and increase the difficulty of detection. A signed fingermark detection method based on deep residual networks and a decision-level fusion strategy was proposed to defend against spoofing attacks from fake fingermarks. Firstly, the multi-scale structure was introduced in the residual module, which improved the network’s depth and breadth without increasing the parameters. Then, the multi-probability label strategy was refined and employed to enhance the local encoding ability of the feature extraction. A score fusion strategy was designed, with weights allocated based on the difference in signed interference levels of local image blocks. Finally, a model fusion strategy based on evidence theory was suggested, which improved detection accuracy by leveraging complementarity between models. A large-scale fingermark database was established, which included real fingermarks made from real fingers and fake fingermarks made from various materials, and this was divided into two sub databases: signed and unsigned. The experimental results show that the proposed method achieves 96.16% accuracy based on the fingerprint dataset of the global liveness detection competition called LivDet2017 and achieves 99.30% accuracy based on the signed fingermark database, while it has good resistance to spoofing attacks from unknown materials. Full article

(This article belongs to the Special Issue Artificial Intelligence for Attack Detection, Financial Services, and Biometrics)

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21 pages, 435 KiB

Open AccessArticle

Integrating Merkle Trees with Transformer Networks for Secure Financial Computation

by Xinyue Wang, Weifan Lin, Weiting Zhang, Yiwen Huang, Zeyu Li, Qian Liu, Xinze Yang, Yifan Yao and Chunli Lv

Appl. Sci. 2024, 14(4), 1386; https://doi.org/10.3390/app14041386 - 08 Feb 2024

Viewed by 541

Abstract

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, [...] Read more.

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing. Full article

(This article belongs to the Special Issue Artificial Intelligence for Attack Detection, Financial Services, and Biometrics)

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18 pages, 1153 KiB

Open AccessArticle

Finsformer: A Novel Approach to Detecting Financial Attacks Using Transformer and Cluster-Attention

by Hao An, Ruotong Ma, Yuhan Yan, Tailai Chen, Yuchen Zhao, Pan Li, Jifeng Li, Xinyue Wang, Dongchen Fan and Chunli Lv

Appl. Sci. 2024, 14(1), 460; https://doi.org/10.3390/app14010460 - 04 Jan 2024

Viewed by 859

Abstract

This paper aims to address the increasingly severe security threats in financial systems by proposing a novel financial attack detection model, Finsformer. This model integrates the advanced Transformer architecture with the innovative cluster-attention mechanism, dedicated to enhancing the accuracy of financial attack behavior [...] Read more.

This paper aims to address the increasingly severe security threats in financial systems by proposing a novel financial attack detection model, Finsformer. This model integrates the advanced Transformer architecture with the innovative cluster-attention mechanism, dedicated to enhancing the accuracy of financial attack behavior detection to counter complex and varied attack strategies. A key innovation of the Finsformer model lies in its effective capture of key information and patterns within financial transaction data. Comparative experiments with traditional deep learning models such as RNN, LSTM, Transformer, and BERT have demonstrated that Finsformer excels in key metrics such as precision, recall, and accuracy, achieving scores of 0.97, 0.94, and 0.95, respectively. Moreover, ablation studies on different feature extractors further confirm the effectiveness of the Transformer feature extractor in processing complex financial data. Additionally, it was found that the model’s performance heavily depends on the quality and scale of data and may face challenges in computational resources and efficiency in practical applications. Future research will focus on optimizing the Finsformer model, including enhancing computational efficiency, expanding application scenarios, and exploring its application on larger and more diversified datasets. Full article

(This article belongs to the Special Issue Artificial Intelligence for Attack Detection, Financial Services, and Biometrics)

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35 pages, 1128 KiB

Open AccessArticle

A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities

by Mehmed Taha Aras and Mehmet Amac Guvensan

Appl. Sci. 2023, 13(24), 13121; https://doi.org/10.3390/app132413121 - 09 Dec 2023

Viewed by 971

Abstract

Although the most widely studied datasets in fraud-detection systems belong to the banking sector, the aviation industry is susceptible to fraud activities that seriously harm airline companies. Therefore, big airline companies have started to purchase or develop their own fraud-detection systems in order [...] Read more.

Although the most widely studied datasets in fraud-detection systems belong to the banking sector, the aviation industry is susceptible to fraud activities that seriously harm airline companies. Therefore, big airline companies have started to purchase or develop their own fraud-detection systems in order to prevent their financial loss and prestige decline. Chronological order and temporal flow are intrinsically of high importance in fraud detection in the banking sector as well as in airline sale channels. Therefore, the transactions in the datasets used in fraud-detection systems should be evaluated not only according to the information they contain but also according to the past transactions they are linked to. One of the best ways to raise awareness about the connected past transactions to the fraud-detection system is to profile the data fields whose historical data is important and dynamically place these profiles on each transaction. In this study, we first draw the baseline, i.e., the first touch in this field, for fraud detection in aviation and then introduce a novel multi-modal profiling mechanism based on deep learning for the detection of fraudulent airline ticket activities. We achieved great success by feeding the new features obtained from those profiles into a deep neural network that is fine-tuned by adjusting the well-known hyperparameters regarding the aviation data. Thanks to the combination of profiling and deep learning, the F1 score of the proposed system reaches up to 89.3% and 93.2% in terms of quantity-based success and cost-based success, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence for Attack Detection, Financial Services, and Biometrics)

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19 pages, 1208 KiB

Open AccessArticle

Advancing Financial Forecasts: A Deep Dive into Memory Attention and Long-Distance Loss in Stock Price Predictions

by Shijie Yang, Yining Ding, Boyu Xie, Yingyi Guo, Xinyao Bai, Jundong Qian, Yunxuan Gao, Wuxiong Wang and Jinzheng Ren

Appl. Sci. 2023, 13(22), 12160; https://doi.org/10.3390/app132212160 - 09 Nov 2023

Cited by 1 | Viewed by 1137

Abstract

In the context of the rapid evolution of financial markets, the precise prediction of stock prices has become increasingly complex and challenging, influenced by a myriad of factors including macroeconomic indicators, company financial conditions, and market sentiment. A model integrating modern machine learning [...] Read more.

In the context of the rapid evolution of financial markets, the precise prediction of stock prices has become increasingly complex and challenging, influenced by a myriad of factors including macroeconomic indicators, company financial conditions, and market sentiment. A model integrating modern machine learning techniques has been introduced in this study, aimed at enhancing the accuracy of stock price prediction. To more effectively capture long-term dependencies in time series data, a novel memory attention module has been innovatively integrated and a unique long-distance loss function has been designed. Through a series of experimental validations, the effectiveness and superiority of this model in the realm of stock price prediction have been demonstrated, especially evident in the

R^{2}

evaluation metric, where an impressive score of 0.97 has been achieved. Furthermore, the purpose, methodology, data sources, and key results of this research have been elaborately detailed, aiming to provide fresh perspectives and tools for the field of stock price prediction and lay a solid foundation for future related studies. Overall, this research has not only enhanced the accuracy of stock price prediction but also made innovative contributions in terms of methodology and practical applications, bringing new thoughts and possibilities to the domain of financial analysis and prediction. Full article

(This article belongs to the Special Issue Artificial Intelligence for Attack Detection, Financial Services, and Biometrics)

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21 pages, 621 KiB

Open AccessArticle

A Reinforcement Learning Framework with Oversampling and Undersampling Algorithms for Intrusion Detection System

by Najmeh Abedzadeh and Matthew Jacobs

Appl. Sci. 2023, 13(20), 11275; https://doi.org/10.3390/app132011275 - 13 Oct 2023

Viewed by 994

Abstract

Intrusion detection systems (IDSs) play a pivotal role in safeguarding networks and systems against malicious activities. However, the challenge of imbalanced datasets significantly impacts IDS research, skewing learning models towards the majority class and diminishing accuracy for the minority class. This study introduces [...] Read more.

Intrusion detection systems (IDSs) play a pivotal role in safeguarding networks and systems against malicious activities. However, the challenge of imbalanced datasets significantly impacts IDS research, skewing learning models towards the majority class and diminishing accuracy for the minority class. This study introduces the Reinforcement Learning (RL) Framework with Oversampling and Undersampling Algorithm (RLFOUA) to address imbalanced datasets. RLFOUA combines RL with diverse resampling algorithms, creating an adaptive learning environment. It integrates the novel True False Rate Synthetic Minority Oversampling Technique (TFRSMOTE) algorithm, emphasizing data-level approaches. Additionally, RLFOUA employs a cost-sensitive approach based on classification metrics. Using the CSE-CIC-IDS2018 and NSL-KDD datasets, RLFOUA demonstrates substantial improvement over existing resampling techniques. Achieving an accuracy of 0.9981 for NSL-KDD and 0.9846 for CSE-CIC-IDS2018, the framework’s performance is evaluated using F1 score, accuracy, precision, recall, and a proposed Index Metric (IM). RLFOUA presents a significant advancement in addressing class imbalance challenges in IDS. It shows an average accuracy improvement of 21.5% compared to the recent resampling technique AESMOTE on the NSL-KDD dataset. Full article

(This article belongs to the Special Issue Artificial Intelligence for Attack Detection, Financial Services, and Biometrics)

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15 pages, 1072 KiB

Open AccessArticle

An Improved LSTM-Based Failure Classification Model for Financial Companies Using Natural Language Processing

by Zhan Wang, Soyeon Kim and Inwhee Joe

Appl. Sci. 2023, 13(13), 7884; https://doi.org/10.3390/app13137884 - 05 Jul 2023

Cited by 3 | Viewed by 1088

Abstract

The Korean e-commerce market represents a large percentage of the global retail distribution market, a market that continues to grow each year, and online payments are rapidly becoming a mainstream payment method. As e-commerce becomes more active, many companies that support electronic payments [...] Read more.

The Korean e-commerce market represents a large percentage of the global retail distribution market, a market that continues to grow each year, and online payments are rapidly becoming a mainstream payment method. As e-commerce becomes more active, many companies that support electronic payments are increasing the number of franchisees. Electronic payments have become an indispensable part of people’s lives. However, the types of statistical information on the results of electronic payment transactions are not consistent across companies, and it is difficult to automatically determine the error status of a transaction if no one directly confirms the error messages generated during payment. To address these issues, we propose an optimized LSTM model. In this study, we classify the error content in statistical information based on natural language processing to determine the error status of the current failed transaction. We collected 11,865 response messages from various vendors and financial companies and labelled them with an LSTM classifier model to create a dataset. We then trained this dataset with simple RNN, LSTM, and GRU models and compared their performance. The results show that the optimized LSTM model with the attention layer added to the dropout layer and the bidirectional recursive layer achieves an accuracy of about 92% or more. When the model is applied to e-commerce services, any error in the transaction status of the system can be automatically detected by the model. Full article

(This article belongs to the Special Issue Artificial Intelligence for Attack Detection, Financial Services, and Biometrics)

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29 pages, 14418 KiB

Open AccessArticle

Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics

by Abdulwahid Al Abdulwahid

Appl. Sci. 2023, 13(12), 7288; https://doi.org/10.3390/app13127288 - 19 Jun 2023

Cited by 1 | Viewed by 3059

Abstract

Ethnic conflicts frequently lead to violations of human rights, such as genocide and crimes against humanity, as well as economic collapse, governmental failure, environmental problems, and massive influxes of refugees. Many innocent people suffer as a result of violent ethnic conflict. People’s ethnicity [...] Read more.

Ethnic conflicts frequently lead to violations of human rights, such as genocide and crimes against humanity, as well as economic collapse, governmental failure, environmental problems, and massive influxes of refugees. Many innocent people suffer as a result of violent ethnic conflict. People’s ethnicity can pose a threat to their safety. There have been many studies on the topic of how to categorize people by race. Until recently, the majority of the work on face biometrics had been conducted on the problem of person recognition from a photograph. However, other softer biometrics such as a person’s age, gender, race, or emotional state are also crucial. The subject of ethnic classification has many potential uses and is developing rapidly. This study summarizes recent advances in ethnicity categorization by utilizing efficient models of convolutional neural networks (CNNs) and focusing on the central portion of the face alone. This article contrasts the results of two distinct CNN models. To put the suggested models through their paces, the study employed holdout testing on the MORPH and FERET datasets. It is essential to remember that this study’s results were generated by focusing on the face’s central region alone, which saved both time and effort. Classification into four classes was achieved with an accuracy of 85% using Model A and 86% using Model B. Consequently, classifying people according to their ethnicity as a fundamental part of the video surveillance systems used at checkpoints is an excellent concept. This categorization statement may also be helpful for picture-search queries. Full article

(This article belongs to the Special Issue Artificial Intelligence for Attack Detection, Financial Services, and Biometrics)

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