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Cybersecurity Threats in FinTech: A Systematic Review

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arXiv 2023年

作者： Javaheri, Danial Fahmideh, Mahdi Chizari, Hassan Lalbakhsh, Pooia Hur, Junbeom Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of School of Business University of Southern Queensland SpringfieldQLD4300 Australia School of Business Computing and Social Sciences University of Gloucestershire The Park CampusCheltenham GloucesterGL50 2RH United Kingdom Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University ClaytonVIC3168 Australia

The rapid evolution of the Smart-everything movement and Artificial Intelligence (AI) advancements have given rise to sophisticated cyber threats that traditional methods cannot counteract. Cyber threats are extremely critical in financial technology (FinTech) as a data-centric sector expected to provide 24/7 services. This paper introduces a novel and refined taxonomy of security threats in FinTech and conducts a comprehensive systematic review of defensive strategies. Through PRISMA methodology applied to 74 selected studies and topic modeling, we identified 11 central cyber threats, with 43 papers detailing them, and pinpointed 9 corresponding defense strategies, as covered in 31 papers. This in-depth analysis offers invaluable insights for stakeholders ranging from banks and enterprises to global governmental bodies, highlighting both the current challenges in FinTech and effective countermeasures, as well as directions for future research. © 2023, CC BY.

关键词： Cybersecurity

An ensemble deep learning model for predicting minimum inhibitory concentrations of antimicrobial peptides against pathogenic bacteria

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iscience 2024年第9期27卷

作者： Chung, Chia-Ru Chien, Chung-Yu Tang, Yun Wu, Li-Ching Hsu, Justin Bo-Kai Lu, Jang-Jih Lee, Tzong-Yi Bai, Chen Horng, Jorng-Tzong Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Sciences and Engineering National Central University Taoyuan Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Department of Laboratory Medicine Chang Gung Memorial Hospital at Linkou Taoyuan City Taiwan School of Medicine Chang Gung University Taoyuan City Taiwan Department of Medical Biotechnology and Laboratory Science Chang Gung University Taoyuan City Taiwan Center for Intelligent Drug Systems and Smart Biodevices (IDS2B) National Yang Ming Chiao Tung University Hsinchu City Taiwan Warshel Institute for Computational Biology School of Medicine The Chinese University of Hong Kong (Shenzhen) Shenzhen 518172 Hong Kong

The rise of antibiotic resistance necessitates effective alternative therapies. Antimicrobial peptides (AMPs) are promising due to their broad inhibitory effects. This study focuses on predicting the minimum inhibitory concentration (MIC) of AMPs against whom-priority pathogens: Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853. We developed a comprehensive regression model integrating AMP sequence-based and genomic features. Using eight AI-based architectures, including deep learning with protein language model embeddings, we created an ensemble model combining bi-directional long short-term memory (BiLSTM), convolutional neural network (CNN), and multi-branch model (MBM). The ensemble model showed superior performance with Pearson correlation coefficients of 0.756, 0.781, and 0.802 for the bacterial strains, demonstrating its accuracy in predicting MIC values. This work sets a foundation for future studies to enhance model performance and advance AMP applications in combating antibiotic resistance. © 2024 The Authors

关键词： Biological sciences Chemistry computer science

Distributed Approach to Traffic Management Automation Implemented According to IEC 61499 11th

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Distributed Approach to Traffic Management Automation Implem...

11th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020

作者： Elkin, Dmitry Vyatkin, Valeriy Institute of Computer Technologies and Information Security Southern Federal University Chekhova. 2 Taganrog347900 Russia Department of Electrical Engineering and Automation Aalto University Otakaari 1B Aalto00076 Finland Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9783030451233

The number of vehicles on public roads is increasing while the road infrastructure is not keeping up to this. It is necessary to apply advanced algorithms and approaches to transport management to maximize the use of the existing road network and increase the capacity of roads. In this article, we propose a way to control traffic flows and automate road infrastructure using a multi-agent approach. The proposed approach involves the distributed management of various elements on the road network and their direct relationship with each other in a peer to peer manner. To implement this concept, we used the open standard of distributed control and automation systems IEC 61499, and to validate the approach we used the SUMO - microscopic and continuous road traffic simulation package. © IFIP International Federation for Information Processing 2020.

关键词： Multi agent systems

PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement

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arXiv 2023年

作者： Huang, Zhen Li, Han Shao, Shitong Zhu, Heqin Hu, Huijie Cheng, Zhiwei Wang, Jianji Zhou, S. Kevin Computer Science Department University of Science and Technology of China Hefei230026 China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Southeast University Nanjing China Institute of Computing Technology CAS Beijing100190 China Z2Sky Technologies Inc. Suzhou215123 China Affiliated Hospital of Guizhou Medical University Guiyang550000 China

The pelvis, the lower part of the trunk, supports and balances the trunk. Landmark detection from a pelvic X-ray (PXR) facilitates downstream analysis and computer-assisted diagnosis and treatment of pelvic diseases. Although PXRs have the advantages of low radiation and reduced cost compared to computed tomography (CT) images, their 2D pelvis-tissue superposition of 3D structures confuses clinical decision-making. In this paper, we propose a PELvis Extraction (PELE) module that utilizes 3D prior anatomical knowledge in CT to guide and well isolate the pelvis from PXRs, thereby eliminating the influence of soft tissue. We conduct an extensive evaluation based on two public datasets and one private dataset, totaling 850 PXRs. The experimental results show that the proposed PELE module significantly improves the accuracy of PXRs landmark detection and achieves state-of-the-art performances in several benchmark metrics, thus better serving downstream tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

Improved MDL Estimators Using Fiber Bundle of Local Exponential Families for Non-exponential Families

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arXiv 2023年

作者： Miyamoto, Kohei Barron, Andrew R. Takeuchi, Jun'ichi Cybersecurity Laboratory Cybersecurity Research Institute National Institute of Information and Communications Technology 4-2-1 Nukui-Kitamachi Koganei Tokyo184-8795 Japan Department of Statistics and Data Science Yale University Kline Tower 219 Prospect Street New HavenCT06511 United States Department of Electrical Engineering and Computer Science Kyushu University 744 Motooka Nishi-ku Fukuoka Fukuoka819-0395 Japan

Minimum Description Length (MDL) estimators, using two-part codes for universal coding, are analyzed. For general parametric families under certain regularity conditions, we introduce a two-part code whose regret is close to the minimax regret, where regret of a code with respect to a target family M is the difference between the code length of the code and the ideal code length achieved by an element in M. This is a generalization of the result for exponential families by Grünwald. Our code is constructed by using an augmented structure of M with a bundle of local exponential families for data description, which is not needed for exponential families. This result gives a tight upper bound on risk and loss of the MDL estimators based on the theory introduced by Barron and Cover in 1991. Further, we show that we can apply the result to mixture families, which are a typical example of non-exponential families. Copyright © 2023, The Authors. All rights reserved.

关键词： Data description

Performance Analysis and Enhancement of Beamforming Training in 802.11ad

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arXiv 2023年

作者： Wu, Wen Cheng, Nan Zhang, Ning Yang, Peng Aldubaikhy, Khalid Shen, Xuemin The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada State Key Lab. of ISN The School of Telecommunications Engineering Xidian University Xian710071 China The Department of Computing Sciences Texas A&M University Corpus ChristiTX78412 United States The School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical Engineering Qassim University Buraydah52571 Saudi Arabia

Beamforming (BF) training is crucial to establishing reliable millimeter-wave communication connections between stations (STAs) and an access point. In IEEE 802.11ad BF training protocol, all STAs contend for limited BF training opportunities, i.e., associated BF training (A-BFT) slots, which results in severe collisions and significant BF training latency, especially in dense user scenarios. In this paper, we first develop an analytical model to evaluate the BF training protocol performance. Our analytical model accounts for various protocol components, including user density, the number of A-BFT slots, and protocol parameters, i.e., retry limit and contention window size. We then derive the average successful BF training probability, the BF training efficiency and latency. Since the derived BF training efficiency is an implicit function, to reveal the relationship between system parameters and BF training performance, we also derive an approximate expression of BF training efficiency. Theoretical analysis indicates that the BF training efficiency degrades drastically in dense user scenarios. To address this issue, we propose an enhancement scheme which adaptively adjusts the protocol parameters in tune with user density, to improve the BF training performance in dense user scenarios. Extensive simulations are carried out to validate the accuracy of the developed analytical model. In addition, simulation results show that the proposed enhancement scheme can improve the BF training efficiency by 35% in dense user scenarios. © 2023, CC BY-NC-SA.

关键词： Analytical models

Iot in traffic management: review of existing methods of road traffic regulation 9th

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Iot in traffic management: review of existing methods of roa...

9th computer science On-line Conference, CSOC 2020

作者： Elkin, Dmitry Vyatkin, Valeriy Southern Federal University Institute of Computer Technologies and Information Security Chekhova. 2 Taganrog347900 Russia Department of Electrical Engineering and Automation Aalto University Otakaari 1B Aalto00076 Finland Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9783030519735

Internet of Things (IoT) is an important upcoming technology for making infrastructures of our society smart and adaptable to the users’ needs. One such infrastructure is transportation. This article discusses existing methods and algorithms for automated management of traffic flows with a purpose of identifying hot spots and methods for applying IoT in this sector. Advantages and disadvantages of the existing methods are discussed, including evaluation of their effectiveness. © Springer Nature Switzerland AG 2020.

关键词： Internet of things

A Data-Driven Method for Spatially-Resolved Thermometry from Line-of-Sight Emission Spectroscopy

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SSRN

SSRN 2022年

作者： Kang, Ruiyuan Kyritsis, Dimitrios C. Liatsis, Panos Department of Mechanical Engineering Khalifa University Abu Dhabi127766 United Arab Emirates Research Innovation Center of Hydrogen and CO2 Khalifa University Abu Dhabi127766 United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127766 United Arab Emirates

A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and machine learning provides better performance than the direct use of CNN. Notably, feature engineering which is comprised of physicsguided transformation, signal representation-based feature extraction and Principal Component Analysis is found to be the most effective. Moreover, it is shown that when using the extracted features, the ensemble-based, light blender learning model offers the best performance with RMSE, RE, RRMSE and R values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method, based on feature engineering and the light blender model, is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown. © 2022, The Authors. All rights reserved.

关键词： Temperature measurement

Multi-camera motion compensation for remote monitoring of heart rate and respiration

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Multi-camera motion compensation for remote monitoring of he...

Imaging Systems and Applications, ISA 2021 - Part of OSA Imaging and Applied Optics Congress 2021

作者： Kuramoto, Lindsay Chu, Ryan Ringuette, Dene Zhu, Eric Mihailidis, Alex Levi, Ofer Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoON Canada Institute of Biomaterials and Biomedical Engineering University of Toronto TorontoON Canada Krembil Research Institute TorontoONM5T 0S8 Canada Toronto Rehabilitation Institute – University Health Network TorontoONM5G 2A2 Canada Department of Occupational Science and Occupational Therapy University of Toronto TorontoON Canada

We demonstrate a motion-tolerant vital signs monitoring using multiple, depth sensing cameras. Results show successful isolation of the respiratory waveform from walking motion and successful isolation of heart rate f... 详细信息

ISBN: (纸本)9781557528209

关键词： Cameras