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Blockchain Solution For SMEs Cybersecurity Threats In E-Commerce

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Blockchain Solution For SMEs Cybersecurity Threats In E-Comm...

computer Science and Emerging Technologies (CSET), International Conference on

作者： Firas H. Zawaideh Waheeb Abu-Ulbeh Salameh A. Mjlae Yousef A. Baker El-Ebiary Yazeed Al Moaiad Sunanda Das Department of Cyber Security Faculty of Science and Informaiton Technology Irbid National University Irbid Jordan Cybersecurity Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine CS Department Prince Abdullah bin Ghazi Faculty of Information Technology Al-Balqa Applied University (BAU) Jordan Faculty of Informatics and Computing UniSZA Malaysia Faculty of Computer and Information Technology MEDIU Malaysia School of Computer Science and Engineering Jain University Bangalore India

The rapid growth of electronic commerce (e-commerce) has revolutionized the way Small and Medium-sized Enterprises (SMEs) conduct business, enabling them to reach global markets and unlock new growth opportunities. However, this digital transformation has also exposed SMEs to an escalating array of cybersecurity threats that could compromise their data, financial integrity, and reputation. Blockchain technology, known for its decentralized and immutable nature, has emerged as a potential solution to enhance the security and resilience of e-commerce operations for SMEs. This research paper aims to investigate the current state of cybersecurity preparedness among SMEs engaged in e-commerce and identify the prevalent threats they face in the digital landscape. To achieve this goal, the study incorporates systematic literature review (SLR) method to examine the best technological solution to address the cybersecurity issues faced by e-commerce organizations in this paper of SMEs operating in e-commerce to gather data on their security practices, incident history, and perceptions of cybersecurity risks. In the context of this research, blockchain technology will be explored as a potential mechanism to enhance the security of e-commerce operations for SMEs. The inherent features of blockchain, such as decentralization, transparency, and immutability, could help protect sensitive data and thwart cyberattacks. By exploring the integration of blockchain into e-commerce systems, the research aims to shed light on the potential benefits and challenges associated with its adoption by SMEs. The research findings will not only shed light on the prevailing cybersecurity practices within SMEs and highlight their vulnerabilities but will also explore how blockchain technology can be leveraged to address these challenges. By understanding these patterns and exploring the feasibility of blockchain integration, the research aims to provide comprehensive recommendations for enhancing cy

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Low c-differentially uniform functions via an extension of Dillon's switching method

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arXiv 2022年

作者： Li, Chunlei Riera, Constanza Stănică, Pantelimon Department of Informatics University of Bergen Bergen5020 Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Department of Applied Mathematics Naval Postgraduate School MontereyCA93943-5212 United States

In this paper we generalize Dillon's switching method to characterize the exact c-differential uniformity of functions constructed via this method. More precisely, we modify some PcN/APcN and other functions with known c-differential uniformity in a controllable number of coordinates to render more such functions. We present several applications of the method in constructing PcN and APcN functions with respect to all c ≠ 1. As a byproduct, we generalize some result of [Y. Wu, N. Li, X. Zeng, New PcN and APcN functions over finite fields, Designs Codes Crypt. 89 (2021), 2637-2651]. Computational results rendering functions with low differential uniformity, as well as, other good cryptographic properties are sprinkled throughout the paper. Copyright © 2022, The Authors. All rights reserved.

关键词： Cryptography

Lung cancer prediction using multi-gene genetic programming by selecting automatic features from amino acid sequences

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Computational Biology and Chemistry 2022年第0期98卷 107638-107638页

作者： Sattar, Mohsin Majid, Abdul Kausar, Nabeela Bilal, Muhammad Kashif, Muhammad Biomedical Informatics Research Lab Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan MIS Division Pakistan Institute of Nuclear Science and Technology Islamabad Pakistan Department of Computing and Technology Iqra University Islamabad Pakistan

Lung cancer is one of the leading causes of cancer related deaths. Early diagnosis of lung cancer using automatic feature selection from large number of features is a challenging task. Conventionally, cancer diagnosis approaches use physical features that appear in later stages, while harmful effects have already been occurred due to abnormal somatic mutations. In order to extract useful novel patterns to efficiently predict cancer at early stages, we analyzed lung cancer related mutated genes that reveal useful information in protein amino acid sequences. For this, we developed a new evolutionary learning technique with biologically inspired multi-gene genetic programming algorithm using discriminant information of protein amino acids. The proposed model efficiently selects 23 discriminant features out of 1500 features. Then it combines the selected features and related primitive functions optimally for prediction of lung cancer. Hence, an efficient predictive model is constructed that helps in understanding the complex heterogeneous nature of lung cancer. The proposed system achieved area under ROC curve and accuracy values of 98.79% and 95.67%, respectively outperforming related lung cancer prediction approaches. © 2022 Elsevier Ltd

关键词： Proteins

Should I stay or should I go: Predicting changes in cluster membership

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arXiv 2021年

作者： Tsoukanara, Evangelia Koloniari, Georgia Pitoura, Evaggelia Department of Applied Informatics University of Macedonia Thessaloniki Greece Computer Science & Engineering University of Ioannina Ioannina Greece

Most research on predicting community evolution focuses on changes in the states of communities. Instead, we focus on individual nodes and define the novel problem of predicting whether a specific node stays in the same cluster, moves to another cluster or drops out of the network. We explore variations of the problem and propose appropriate classification features based on local and global node measures. Motivated by the prevalence of machine learning approaches based on embeddings, we also introduce efficiently computed distance-based features using appropriate node embeddings. In addition, we consider chains of features to capture the history of the nodes. Our experimental results depict the complexity of the different formulations of the problem and the suitability of the selected features and chain lengths. © 2021, CC BY.

关键词： Classification (of information)

Automated Fire Hydrant Monitoring and Maintenance using IoT Data Analytics and Naive Bayes Classifier

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Automated Fire Hydrant Monitoring and Maintenance using IoT ...

Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： R. Sarasu V. Nandagopal N V Shibu Suresh Lakshminarayanan T Saju Raj M. Rajmohan Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Electrical and Electronics Engineering School of Engineering Mohan Babu University Tirupathi India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Engineering & Electronics University of Technology and Applied Sciences South Al Batina Oman Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This paper introduces a new method for managing fire hydrants that uses a monitoring system based on the Internet of Things (IoT) and a Naive Bayes Classifier (NBC) for predictive maintenance. Water pressure, flow rate, and the physical state of fire hydrants are among the essential factors for the proposed system continually collecting data using an IoT sensor network. The system analyzes real-time data using NBC. These predictive capabilities reduce the potential of hydrant breakdowns during emergencies through preventive maintenance potential. It introduces a reliable IoT infrastructure for fire hydrant monitoring, improving the acquired data's granularity and timeliness. NBC provides a powerful instrument for early problem detection, significantly improving maintenance effectiveness. Since the technology is compatible with existing fire department procedures, it can easily integrate into current operational processes. Better public safety, more efficient use of resources, and more dependable fire hydrants are potential outcomes of responsive and proactive data-driven maintenance techniques. The findings improve overall fire safety and operational efficiency, with a 25% decrease in maintenance expenditures and a 40% increase in issue detection accuracy.

关键词： Accuracy Scalability Urban areas Real-time systems Maintenance Naive Bayes methods Internet of Things Stakeholders Predictive maintenance Risk mitigation

Wind turbine condition monitoring based on intra- and inter-farm federated learning

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arXiv 2024年

作者： Grataloup, Albin Jonas, Stefan Meyer, Angela Bern University of Applied Sciences School of Engineering and Computer Science Quellgasse 21 Biel2501 Switzerland Università della Svizzera italiana Faculty of Informatics Via la Santa 1 Viganello Lugano6962 Switzerland Delft University of Technology Department of Geoscience and Remote Sensing Stevinweg 1 Delft2628 Netherlands

As wind energy adoption is growing, ensuring the efficient operation and maintenance of wind turbines becomes essential for maximizing energy production and minimizing costs and downtime. Many AI applications in wind energy, such as in condition monitoring and power forecasting, may benefit from using operational data not only from individual wind turbines but from multiple turbines and multiple wind farms. Collaborative distributed AI which preserves data privacy holds a strong potential for these applications. Federated learning has emerged as a privacy-preserving distributed machine learning approach in this context. We explore federated learning in wind turbine condition monitoring, specifically for fault detection using normal behaviour models. We investigate various federated learning strategies, including collaboration across different wind farms and turbine models, as well as collaboration restricted to the same wind farm and turbine model. Our case study results indicate that federated learning across multiple wind turbines consistently outperforms models trained on a single turbine, especially when training data is scarce. Moreover, the amount of historical data necessary to train an effective model can be significantly reduced by employing a collaborative federated learning strategy. Finally, our findings show that extending the collaboration to multiple wind farms may result in inferior performance compared to restricting learning within a farm, specifically when faced with statistical heterogeneity and imbalanced datasets. © 2024, CC BY-NC-ND.

关键词： Federated learning

Pre-trained Molecular Language Models with Random Functional Group Masking

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arXiv 2024年

作者： Peng, Tianhao Li, Yuchen Li, Xuhong Bian, Jiang Xie, Zeke Sui, Ning Mumtaz, Shahid Xu, Yanwu Kong, Linghe Xiong, Haoyi Baidu Research Baidu Inc Haidian Beijing100085 China School of Computer Science and Engineering Beihang University Haidian Beijing100091 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Minhang200240 China Guangdong Guangzhou511455 China Department of Molecular and Structural Biochemistry North Carolina State University RaleighNC27695 United States Department of Computer Science Nottingham Trent University Nottingham United Kingdom Department of Applied Informatics Silesian University of Technology Gliwice Poland School of Future Technology South China University of Technology Guangdong Guangzhou510641 China

Recent advancements in computational chemistry have leveraged the power of transformer-based language models, such as MoLFormer, pre-trained using a vast amount of simplified molecular-input line-entry system (SMILES) sequences, to understand and predict molecular properties and activities, a critical step in fields like drug discovery and materials science. To further improve performance, researchers have introduced graph neural networks with graph-based molecular representations, such as GEM, incorporating the topology, geometry, 2D or even 3D structures of molecules into pre-training. While most of molecular graphs in existing studies were automatically converted from SMILES sequences, it is to assume that transformer-based language models might be able to implicitly learn structure-aware representations from SMILES sequences. In this paper, we propose MLM-FG – a SMILES-based Molecular Language Model, which randomly masking SMILES subsequences corresponding to specific molecular Functional Groups to incorporate structure information of atoms during the pre-training phase. This technique aims to compel the model to better infer molecular structures and properties, thus enhancing its predictive capabilities. Extensive experimental evaluations across 11 benchmark classification and regression tasks in the chemical domain demonstrate the robustness and superiority of MLM-FG. Our findings reveal that MLM-FG outperforms existing pre-training models, either based on SMILES or graphs, in 9 out of the 11 downstream tasks, ranking as a close second in the remaining ones. Remarkably, MLM-FG also surpasses 3D-graph-based models, which explicitly incorporate molecular structures into their inputs, highlighting its exceptional capacity for representation learning even without explicit 3D structural information. These results indicate that MLM-FG effectively captures the nuanced language of molecules, offering promising implications for computational chemistry and related disci

关键词： Benchmarking

Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

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Automatic detection of lung nodule in CT scan slices using C...

2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： computerized tomography

A Fully Programmable On-Chip Planar Waveguide for Machine Learning

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A Fully Programmable On-Chip Planar Waveguide for Machine Le...

Conference on Lasers and Electro-Optics (CLEO)

作者： Martin M. Stein Tatsuhiro Onodera Benjamin A. Ash Mandar M. Sohoni Melissa Bosch Ryotatsu Yanagimoto Marc Jankowski Timothy P. McKenna Tianyu Wang Gennady Shvets Maxim R. Shcherbakov Logan G. Wright Peter L. McMahon School of Applied and Engineering Physics Cornell University Ithaca NY USA NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale CA USA E. L. Ginzton Laboratory Stanford University Stanford CA USA Department of Electrical Engineering and Computer Science UC Irvine Irvine CA USA Department of Applied Physics Yale University New Haven CT USA Kavli Institute at Cornell for Nanoscale Science Cornell University Ithaca NY USA

We introduce a device containing a planar waveguide whose spatial refractive index profile $n(x, z)$ can be programmed in real time. We demonstrate use this device as an optical neural network.

ISBN: (纸本)9798350369311

We introduce a device containing a planar waveguide whose spatial refractive index profile $n(x, z)$ can be programmed in real time. We demonstrate use this device as an optical neural network.

关键词： Optical planar waveguides Refractive index Waveguide lasers Optical fiber networks Optical variables control Real-time systems Planar waveguides System-on-chip Optical refraction Optical devices