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A Multilayer Approach to the security of Blockchain networks of the Future 22nd

A Multilayer Approach to the Security of Blockchain Networks...

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22nd international conference on Computational Science and its Applications (ICCSA)

作者： Bogdanov, Alexander Degtyarev, Alexander Shchegoleva, Nadezhda Korkhov, Vladimir Khvatov, Valery Zaynalov, Nodir Kiyamov, Jasur Dik, Aleksandr St Petersburg State Univ St Petersburg Russia St Petersburg State Marine Tech Univ St Petersburg Russia DGT Technol AG Toronto ON Canada Tashkent Univ Informat Technol Samarkand Branch Samarkand Uzbekistan

ISBN: (纸本)9783031105364;9783031105357

Decentralized computing and blockchain technology play a significant role in the implementation of modern digital economy business models. The most noticeable trends in this economy are the diversification and convergence of platforms and services, which is often achieved through undesirable fragmentation of the overall IT landscape. Business solutions represented by different blockchain networks turn out to be poorly connected, data exchange between them is difficult. The search for ways to overcome barriers between different decentralized networks leads to an increase in the importance of cross-platform integration solutions, providing the necessary level of interoperability. Such solutions must be secure both in terms of confidentiality and fault tolerance. Below is a vision of the architecture of integration gateways using the ODAP-2PC protocol, which provides crash fault-tolerance for the integration of various networks. This architecture provides transparency of interaction, reliability and continuity of audit in digital asset exchange systems or payment systems with increased requirements for interoperability.

关键词： Blockchain distributed recovery ODAP

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A Combined Weld Net-UNet Segmentation Model for Welded Seam Detection

A Combined Weld Net-UNet Segmentation Model for Welded Seam ...

引用

distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Fenxia Liu Junwang Hu Yinjing Yao Guangxi Science & Technology Normal University Laibin China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Nowadays, Weld Seam (WS) detection is important in the field of intelligent manufacturing of welding as well as in the automated detection of defects. However, for the WS detection, point clouds are used as a major aspect; while the existing models used Combined UNet and WeldNet segmentation for WS detection, but individually UNet does not support tube sheet point clouds while individually WeldNet does not support regular point clouds. Therefore, in this paper, the integration of WeldNet-UNet segmentation model is proposed for the effective detection of WS. Firstly, WS dataset is preprocessed using sparse convolutional middle layers to generate 3D-feature maps from input images. After that, these feature maps are further processed with pre-trained WeldNet model followed by UNet encoder to extract voxel features and corresponding UNet decoder generated binary segmentation output as defective and non-defective WS. From the results, the proposed WeldNet-UNet model performed better compared to existing UNet and Zoom in on Target (ZIOT) in terms of accuracy (98.53 %), jaggard coefficient (96.77 %), Dice Similarity Coefficient (99.58 %) and recall (99.67%) respectively.

关键词： Point cloud compression Image segmentation Accuracy Welding Data preprocessing Feature extraction computer networks Manufacturing Decoding computer security

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Improving Accuracy in Network Intrusion Detection via Machine Learning Algorithms: An In-Depth Examination

Improving Accuracy in Network Intrusion Detection via Machin...

引用

distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Suriya Prakash J Trishlaa S Soniya R V Lakshmanan Kiran S Department of CSE JAIN (Deemed-to-be-University) Ramnagara India Department of Artificial Intelligence and Machine Learning Rajarajeshwari College of Engineering Bengaluru India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Mathematics Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Intrusion detection is essential for securing computer networks by monitoring and analyzing activity to detect and respond to unauthorized access and malicious behavior. By examining network traffic and system logs, intrusion detection systems (IDS) help protect data integrity and availability, enabling swift responses to potential security incidents. In an ever-evolving cyber threat landscape, IDS plays a critical role in maintaining network security. This research investigates the performance of various machine learning algorithms for classification tasks using a dataset from Kyoto collected on December 31st, 2015. The algorithms explored include Naive Bayes, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), AdaBoost, and CatBoost. Training and testing the model with different data splits (80/20, 70/30, 60/40) revealed that AdaBoost, LGBM, XGBoost, Decision Tree, Gradient Boosting, and Random Forest consistently delivered the highest accuracy. The study concludes that selecting the appropriate algorithms and data splits is crucial for enhancing IDS effectiveness, providing valuable insights for future research. Explore the code behind this study at https://***/Trishlaa07/Kyoto_2015_Dec_day31

关键词： Training Machine learning algorithms Accuracy Nearest neighbor methods Boosting Data models Classification algorithms Decision trees Random forests Testing

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vHTTP: Enhancing security for HTTP Requests through Client-Side Mitigation

vHTTP: Enhancing Security for HTTP Requests through Client-S...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Ritesh Mukherjee Roorkee Institute of Technology

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The Hypertext Transfer Protocol (HTTP) is a fundamental protocol for web communication but is often exploited due to its inherent vulnerabilities. Attackers can intercept and manipulate HTTP requests, posing significant security threats. Enhancing the security of HTTP requests on the client side is crucial to mitigating these risks and ensuring secure web interactions. This research investigates various client-side mitigation strategies to enhance the security of HTTP requests. The study evaluates the effectiveness of these strategies by implementing them in a controlled environment and measuring their impact on security and performance. Techniques such as input validation, encryption, tokenization, and security headers are examined. The implementation involves a combination of theoretical analysis and practical experimentation using modern web development tools and frameworks. The findings demonstrate that incorporating client-side mitigation techniques conspicuously improves the security of HTTP requests. Input validation and tokenization effectively prevent common attacks like SQL injection and Cross-Site Request Forgery (CSRF). The use of security headers, which is inclusive of Content security Policy (CSP) and HTTP Strict Transport security (HSTS), enhances the overall security posture by mitigating threats like Cross-Site Scripting (XSS) and man-in-the-middle attacks. The implementation of encryption mechanisms ensures the confidentiality and integrity of data transmitted between clients and servers. This research underlines the importance of adopting comprehensive client-side security measures to protect HTTP requests from malicious activities. By leveraging a combination of input validation, tokenization, security headers, and encryption, web developers can significantly reduce the attack surface and enhance the security of web applications. Future work will focus on further refining these techniques and exploring additional mitigation strategies to address

关键词： Prevention and mitigation Cross-site scripting SQL injection HTTP Tokenization Forgery Software Encryption security Servers

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Design of an Iterative Model Incorporating mBERT, CSLM, and ADAN for Multilingual and Code-Switched Sentiment Analysis

Design of an Iterative Model Incorporating mBERT, CSLM, and ...

引用

distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Kalyani P. Sable S. L. Satarkar CSE Shri Sant Gajanan Maharaj College of Engineering Shegaon India CSE College of Engineering and Technology Akola India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The global spread of code-switched and multilingual content on Twitter highlights the importance of effective sentiment analysis. However, existing algorithms often overlook regional and multilingual tweets, resulting in reduced performance. To address this, our study introduces a model that integrates mBERT, CSLM, and ADAN to enhance sentiment analysis. mBERT, trained on a large bilingual dataset, captures intricate contextual embeddings from both English and Marathi tweets. CSLM is designed to handle the complexities of code-switching in mixed-language content, while ADAN uses adversarial training to reduce biases and improve domain invariance across political, social, and commercial tweets. The combined mBERT-CSLM-ADAN model achieves an accuracy of 89%, outperforming the baseline mBERT and CSLM models. This approach offers a more generalizable, robust, and domain-flexible solution for social media sentiment classification, particularly in multilingual environments.

关键词： Training Sentiment analysis Analytical models Social networking (online) Computational modeling computer networks Multilingual Iterative methods computer security Context modeling

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Reference Structure of Pluggable Service -based New Retail E-Commerce Platform using Web Application

Reference Structure of Pluggable Service -based New Retail E...

引用

distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Xin Jiang Department of Business Dalian University of Finance and Economics Dalian China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The huge growth of internet technologies and major spread of recent and advanced system causes maximum diversity and size in new retail e-commerce device development. The usage of Information and Communications Technology (ICT) have evaluated dramatical every day. The much challenging ICT’s applications have employed named as retail e-commerce platform. The new retail e-commerce platform included the new dimensions for commercial activity, behaviour of consumers in classical activities of commercial in modifications. Hence, the new retail e-commerce platform using web application is needed which suited for advanced requirements to flexible and agile service combination. This article developed the reference structure of effective pluggable service platform-based e-commerce using web application. Here, evaluated the present available solutions for online shop retail e-commerce platform and combination of platforms in market. Based on business method of e-commerce platform, various legacy elements are introduced in application layers. The retailer comes from the business of offline channel with count of bricks and mortar stores in device for controlling their process.

关键词： Performance evaluation Mortar Process control computer networks Information and communication technology Electronic commerce computer security Business

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Intrusion Detection in OT/SCADA Cyber security and Tabular Generative Adversarial networks

Intrusion Detection in OT/SCADA Cyber Security and Tabular G...

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international conference on Developments in esystems Engineering (DESE)

作者： Hillol Biswas Muthyala Manoj Kumar Pawan Kumar Power Division WAPCOS LIMITED Gurgaon India

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

The modern Smart Grid is characterised by the simultaneous flow of power and information in both directions. It is considered a critical infrastructure within the realm of cyber security frameworks. Traditionally, the energy system is composed of many components including generation, transmission, which refers to the country’s national grid, state/regional grid, distribution grid, and the integration of distributed energy resources (DER). The standard cybersecurity ecosystem consists of Network Defence, Application security, Cloud security, and Endpoint security, together with operational technology (OT), supervisory control and data acquisition system (SCADA), Smart Grid, Wide Area Monitoring System/Phasor Measurement Unit, and the Internet of Things (IoT). Intrusion detection in OT/SCADA in power electricity endeavour based on generative learning addresses the core issue of data imbalance in cybersecurity datasets. As cyberthreats and the tactics are becoming increasingly smarter, tabular GAN in network intrusion detection can bolster the cyber defence in the highly complex real-time endeavour.

关键词： Electricity SCADA systems Generative adversarial networks Smart grids distributed power generation Critical infrastructure Internet of Things computer crime Standards Testing

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Enhancing Cybersecurity Through Combined Convolutional Neural Network-Gated Recurrent Unit Approach for distributed Denial of Service Attack Detection

Enhancing Cybersecurity Through Combined Convolutional Neura...

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Innovative Engineering Sciences and Technological Research (ICIESTR), international conference on

作者： Shilpa Choudhary Chris Harry Kandikattu Sandeep Kumar MVV Prasad Kantipudi Munish Kumar Department of Computer Science and Engineering(AI & ML) Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Educational Foundations Vijayawada India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

In cyber-security, integrating GRU with CNN has proven to be a solid and effective method for detecting distributed Denial of Service (DDoS) attacks. This work presents a robust approach that combines the temporal sequence modelling capabilities of GRUs with the spatial feature extraction capabilities of CNNs. Integrating these two deep learning architectures provides a comprehensive solution, allowing precise detection and reduction of harmful activity inside network traffic. This work enhances DDoS detection methods and emphasizes the increasing importance of using advanced deep learning techniques to strengthen cyber-security measures. The suggested methodology effectively tackles the dynamic nature of DDoS attacks by leveraging the strengths of CNNs and GRUs. This approach offers a diverse and efficient way to protect network integrity. Integrating CNN and GRU in DDoS detection is crucial in strengthening network security against emerging attacks in the ever-changing digital ecosystem. This study highlights the flexibility and effectiveness of incorporating sophisticated neural network structures to improve the robustness of cyber-security systems in response to changing threats.

关键词： Neural networks Ecosystems Telecommunication traffic Network security Denial-of-service attack Solids Feature extraction Robustness Convolutional neural networks computer crime

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Construction of a computer Data security Detection Model based on Artificial Intelligence

Construction of a Computer Data Security Detection Model bas...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Ruzhen Ma School of Modern Information Industry Guangzhou College of Commerce Guangzhou China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In the information society, the network has penetrated into all aspects of people's life, work and study, providing great convenience for people. Hackers attack through key devices in the Internet in a very complex way, and the protection of terminal equipment is often difficult to play an effective role due to the limitations of its operating environment, which in turn restricts the subsequent prevention and optimisation of network security. Network traffic anomaly detection methods based on machine learning have gradually been emphasized. This paper introduces a new effective method based on deep learning. Firstly, the service information is transformed into feature vectors with better representativeness through network service data transformation; secondly, the extracted feature vectors are used to model the abnormal behaviors, and the models are trained and validated; finally, the new services are classified into normal and abnormal services. In this paper, a model of CNN-BiGRU (Convolutional Neural Network with Bidirectional Gated Recurrent Unit) combined with an attention mechanism is used to extract local features and time series dependent features. The experimental results show that the model exhibits different performances under different datasets, training set proportions, time steps and optimizer conditions. The experimental data includes a variety of data security related datasets. The results show that when the time step is set to 200, the model achieves an accuracy of 93.0%, a precision of 92.0%, a recall of 94.0%, and an F1 score of 93.0. The study provides an effective technical solution in the field of network data security detection, and provides reliable support and guidance for network security defense.

关键词： Training Analytical models Accuracy Computational modeling Data security Telecommunication traffic Network security Feature extraction Data models Vectors

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Cloud distributed Optimal Control of ESS-PV Residential Inverters

Cloud Distributed Optimal Control of ESS-PV Residential Inve...

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Power systems and Electrical Technology (PSET), IEEE international conference on

作者： Byungchul Kim Eyad H. Abed Department of Electrical and Computer Engineering University of Maryland College Park USA

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

Perfect power flow methods can be employed to choose which inverters will supply and determine their optimal real and reactive power settings based on financial goals. Utilizing advanced semidefinite relaxation make possible to work out with lessen calculation loads. Reflecting on the complex power inculcation originated by strong robustness and the high resilience of ESS-PV inverter, the steady state voltage security area (SSVSA) for inverter is created. The voltage expedition indicator for inverter is suggested to measure the voltage security limit between the security range and operation state. The suggested scheme balances beneficially to large distribution networks.

关键词： Cloud computing Voltage measurement Simulation Energy Internet Inverters Robustness Steady-state security Energy management systems Voltage control

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