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2nd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022

作者： Antonopoulos, Konstantinos Ringas, Dimitrios Christopoulou, Eleni Keramidas, Georgios Antonopoulos, Christos Voros, Nikolaos University of Peloponesse Electrical and Computer Engineering Department Patras Greece Ionian University Department of Informatics Corfu Greece School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9781665498258

ISBN: (纸本)9781665498258

This paper presents the design, development and evaluation of a novel Network Simulator as a Service infrastructure. It is based on a prominent open-source discrete-event network simulator which is extended to offer advanced batch simulation execution, results acquisition, post-processing analysis and presentation features, as well as machine learning optimisations to reduce the overall processing load. In that respect, we have developed a software system, which allows researchers to define simulation scenarios along with a set of parameters and to commission simulation executions in large scale thus delivering to the end user a service that is novel, robust, extendible and extensible. It has been thoroughly evaluated within ESDA Lab (UoP), proving its feasibility and demonstrating its ability to leverage machine learning techniques in order to automatically identify and exclude simulations that are classified as ones that will not meet defined performance requirements, thus drastically reducing simulation workload, delay and resource utilisation. © 2022 IEEE.

关键词： Wireless networks

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On Quantitative Algebraic Higher-Order Theories 7

On Quantitative Algebraic Higher-Order Theories

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7th International Conference on Formal Structures for Computation and Deduction, FSCD 2022

作者： Lago, Ugo Dal Honsell, Furio Lenisa, Marina Pistone, Paolo Department of Computer Science and Engineering University of Bologna Italy Department of Mathematical Sciences Informatics and Physics University of Udine Italy

ISBN: (纸本)9783959772334

We explore the possibility of extending Mardare et al.’s quantitative algebras to the structures which naturally emerge from Combinatory Logic and the λ-calculus. First of all, we show that the framework is indeed applicable to those structures, and give soundness and completeness results. Then, we prove some negative results clearly delineating to which extent categories of metric spaces can be models of such theories. We conclude by giving several examples of non-trivial higher-order quantitative algebras. © Ugo Dal Lago, Furio Honsell, Marina Lenisa, and Paolo Pistone

关键词： Topology

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New Cloud Computing Authentication Based on Secure Hash Algorithm (SHA-3) and Lightweight Sosemanuk Algorithm 2nd

New Cloud Computing Authentication Based on Secure Hash Algo...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Niaf, Jolan Rokan Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Hoomod, Haider K. Salman, Muna Muhsen Iraqi Commission for Computers & Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq College of Education Al-Mustansiriyah University Baghdad Iraq Ministry of Science and Technology Department of Information Technology Baghdad Iraq

ISBN: (纸本)9789819776023

The term "cloud computing" refers to a vast network of linked, virtually hosted, and easily updatable computer resources. That are capable of consisting of a number of different apps and offering a number of different services to customers, and that have the potential to do so. Customers are required to pay for the cost of using the Services in accordance with the company's "pay-for-use only" policy. However, there are certain firms, such as Google Inc, that provide customers with particular free storage space even though the services provided by the company are limited, specifically with regard to the public cloud. A third-party service provider frequently owns and maintains the infrastructure required to run cloud computing services. But because more and more data and files pertaining to individuals and businesses are being stored in the cloud, worries have started to grow about how safe it actually is. Among these are the protection of software for virtualization and distributed computing, the safety of application software, the administration of identities, and the authentication and restriction of access. Nevertheless, efficient user authentication is the most essential need of cloud computing, as it limits unauthorized usage of a cloud server. Cloud computing has become increasingly popular in recent years. In this research, we present a new authentication method between users and online computing systems that is based on the suggested secure hash algorithm (SHA-3) and makes use of the lightweight Sosemanuk method. We created this technique. That system is going to be known as the SoSha3 system. The design that has been proposed for the system deals with data in three different sizes: 128, 256, and 512 bits. In order to make these authentication systems suitable for cloud computing at high rates, some modifications were made to them. The three-dimensional chaotic system, also known as the Lorenz system, will be combined with the authentication architecture tha

关键词： Cloud security

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(ω1,ω2)-temporal random hyperbolic graphs

arXiv

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arXiv 2024年

作者： Zambirinis, Sofoclis Papadopoulos, Fragkiskos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus

We extend a recent model of temporal random hyperbolic graphs by allowing connections and disconnections to persist across network snapshots with different probabilities, ω1 and ω2. This extension, while conceptually simple, poses analytical challenges involving the Appell F1 series. Despite these challenges, we are able to analyze key properties of the model, which include the distributions of contact and intercontact durations, as well as the expected time-aggregated degree. The incorporation of ω1 and ω2 enables more flexible tuning of the average contact and intercontact durations, and of the average time-aggregated degree, providing a finer control for exploring the effect of temporal network dynamics on dynamical processes. Overall, our results provide new insights into the analysis of temporal networks and contribute to a more general representation of real-world scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Network theory (graphs)

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Practical Evaluation of Graph Neural Networks in Network Intrusion Detection

Practical Evaluation of Graph Neural Networks in Network Int...

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2023 Italian Conference on Cyber Security, ITASEC 2023

作者： Venturi, Andrea Pellegrini, Daniele Andreolini, Mauro Ferretti, Luca Marchetti, Mirco Colajanni, Michele Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia Modena Italy Department of Physics Computer Science and Mathematics University of Modena and Reggio Emilia Modena Italy Department of Informatics Science and Engineering University of Bologna Bologna Italy

The most recent proposals of Machine and Deep Learning algorithms for Network Intrusion Detection Systems (NIDS) leverage Graph Neural Networks (GNN). These techniques create a graph representation of network traffic and analyze both network topology and netflow features to produce more accurate predictions. Although prior research shows promising results, they are biased by evaluation methodologies that are incompatible with real-world online intrusion detection. We are the first to identify these issues and to evaluate the performance of a state-of-the-art GNN-NIDS under real-world constraints. The experiments demonstrate that the literature overestimates the detection performance of GNN-based NIDS. Our results analyze and discuss the trade-off between detection delay and detection performance for different types of attacks, thus paving the way for the practical deployment of GNN-based NIDS. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep learning

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Urban Pluvial Flood Prediction Using Machine Learning Models 4

Urban Pluvial Flood Prediction Using Machine Learning Models

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4th International Conference on Innovations in computer Science, ICONICS 2024

作者： Waseemullah Hassan, Tahir Majeed, Sajid Memon, Muhammad Qasim NED University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Institute of Space Technology Department of Remote Sensing & Geo-Informatics Karachi Pakistan University of Sufism and Modern Sciences Department of Information and Computing Bhitshah Pakistan

ISBN: (纸本)9798331504342

Urban pluvial flooding causes threats to human lives, economy, infrastructure, and ecosystems around the world, particularly in developing countries. Pakistan is among those developing countries that have become vulnerable to urban flooding due to global warming, climate change risks, rapid urbanization, and the occurrence of extreme and unusual rainfall events in the monsoon season. To effectively mitigate the adverse effects of pluvial flooding, there is a pressing need for an advanced and accurate predictive model that is capable of predicting urban pluvial flooding. The aim of this research is to utilize the potential of machine learning (ML) techniques and algorithms to accurately predict the occurrence of urban pluvial flooding, for this study we have worked on the monsoon rainfall data of multiple cities of Sindh province in Pakistan. We made an attempt to use multiple ML models especially logistic regression (LR), random forest (RF), k nearest neighbor (KNN), and support vector machine (SVM) to propose the best and accurate predictive model. The developed models were evaluated and assessed using various performance evaluation metrics which includes accuracy, recall, precision and F1 score. The best among proposed models was created using LR which achieved an accuracy of 97%, while the accuracy of other models which were created using SVM, KNN and RF were 96%, 93%, and 92% respectively. © 2024 IEEE.

关键词： Prediction models

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Social engineering Attacks Detection Approach 13

Social Engineering Attacks Detection Approach

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Lysenko, Sergii Vorobiov, Volodymyr Bokhonko, Oleksandr Gaj, Piotr Savenko, Oleg Woloszyn, Jacek Khmelnitsky National University Department of Computer Engineering and Information Systems Khmelnitsky Ukraine Silesian University of Technology Gliwice Poland Khmelnitsky National University Department of Computer Engineering and System Programming Khmelnitsky Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland

ISBN: (纸本)9798350396119

The paper focuses on the problem of technical social engineering attacks that encompass the manipulation of individuals to reveal sensitive information, execute actions, or breach security systems. These exploits frequently capitalize on human psychology, trust, and a lack of vigilance to attain unauthorized entry to networks, systems, or data. In contrast to traditional social engineering tactics that center on psychological manipulation, technical social engineering attacks employ technological means and strategies to manipulate and beguile individuals. The paper presents an attempt to detect social engineering attacks. The approach utilized four machine learning algorithms (decision tree, random forest, K-nearest neighbor, and extreme gradient boosting). The analysis is centered on data collected from network hosts, which may serve as indicators of a potential social engineering attack. The empirical results demonstrated high detection accuracy. © 2023 IEEE.

关键词： Cybersecurity

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Handwritten Signature Recognition Using Siamese Neural Network

Handwritten Signature Recognition Using Siamese Neural Netwo...

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International Conference on computer engineering, Network and Intelligent Multimedia (CENIM)

作者： Yuliana Setiowati Arna Fariza Muhamad Rifqi Luthfi Ramadhan Informatics and Computer Engineering Department Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

This research proposes a novel approach to addressing the issue of signature authenticity identification by implementing Deep Metric Learning (DML). The aim of this study is to develop a DML model with high accuracy in training and testing processes that can be integrated with a mobile application for signature authenticity verification. The data used in this research are signature images from 90 individuals, divided into two categories: genuine signatures and forged signatures. Testing results show that the developed model has an accuracy of 85.7 % , with high precision for the forged signature class at 95.9% and recall for the genuine signature class at 94.9%. The conclusion of this research is that the developed DML model is reliable for signature verification, although further development is needed to improve the model's performance, especially in detecting forged signatures. Suggestions for future research include increasing the amount and variety of training data, integrating the model with a mobile application, and testing the model under various conditions and on different devices.

关键词： Training Handwriting recognition Solid modeling Accuracy Neural networks Training data Solids Data models Mobile applications Testing

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Secure & Traceable Companies File Management System Using Blockchain and IPFS

Secure & Traceable Companies File Management System Using Bl...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Cahyo Arissabarno Sritrusta Sukaridhoto Idris Winarno Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

With the advancement of cloud-based file management platforms, sharing and storing files in the company's area can be easily achieved through a variety of file-sharing platforms. However, these platforms often lack of security and adequate access logs or activity records for the shared files, resulting in a lack of control and traceability for users. This research proposes an traceable and secure system for managing files in companies using blockchain and IPFS. The content ID of uploaded files to IPFS will be stored on the blockchain along with user access rights and access logs. Integration with the existing company file management system is also facilitated by the use of the change data capture method. Experimental findings demonstrate that the system offers the desired traceability of access and activity while also meeting data security requirements such as confidentiality, integrity, and availability. Furthermore, performance tests indicate satisfactory outcomes with 0.8 seconds per MB for file storage on IPFS, and 4.1 seconds per transaction for data storage on Blockchain. The experimental results revealed that the proposed system, integrating blockchain and IPFS, successfully addressed the need for traceability of access and activity for the company's data, providing a significant improvement in data security and management over existing solutions.

关键词： Data security Memory Companies Permission Blockchains

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CO2 sensor placement for the prompt identification of high virus transmission-risk conditions 17

CO2 sensor placement for the prompt identification of high v...

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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Athanasiou, Modestos Kyriacou, Alexis Michaelides, Michalis P. Lelantus Innovations Ltd Nicosia Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

Poor Indoor Air Quality (IAQ) can contribute to airborne viruses remaining viable for longer time periods with the increased risk of infecting more people. Various methodologies, usually by considering CO2 concentrations and well-mixed zones, have been proposed for the indirect assessment of the virus transmission risk. However, IAQ conditions can vary significantly within a room and the position of sensors is crucial to ensure real-time estimation of the virus transmission risk. In this work, a novel sensor placement methodology is presented for the prompt identification of increased virus transmission risk conditions, through the early detection of increased CO2 concentrations in the exhaled air. Specifically, the 3D airflow vectors for multiple ventilation scenarios are used to compute the impact of each possible sensor placement, and the optimal sensor positions are computed using multi-objective optimization. Simulation results are provided for calculating the optimal positions for 1-3 CO2 sensors in a real office-room. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Multiobjective optimization

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