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8th International Conference on Information technology and Nanotechnology, ITNT 2022

作者： Lvovich, Igor Lvovich, Yakov Preobrazhenskiy, Andrey Preobrazhenskiy, Yuriy Sakharov, Yuriy Voronezh Institute of High Technologies Faculty of Informatics Voronezh Russia Voronezh State Technical University Faculty of Information Technology and Computer Security Voronezh Russia Dubna International University Dubna Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The paper deals with the problem associated with the description of the Internet of Things system. The purpose of the paper is to ensure the required efficiency of this system. In the course of describing the problem, it was proposed to use neural networks to divide objects in the Internet of Things system into groups. The illustration of the ART network operation model is given. An appropriate algorithm for dividing objects into groups was developed. The results of modeling on the basis of such an algorithm are given. The dependence of efficiency parameters from variants of system of Internet of Things is obtained. © 2022 IEEE.

关键词： Efficiency

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Detection of SYN Flood Attack Based on Bays Estimator (DsFaBe) in MANET 4

Detection of SYN Flood Attack Based on Bays Estimator (DsFaB...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Rishkan, Basheer Usmani, Muhammad Hamza Sheikh, Md Amin Ullah Jazri, Husin Bin Khan, Navid Ali Sharma, Komal School of Computing and Informatics Albukhary International University Kedah Alor Setar Malaysia School of Computer Science Taylor's University Subang Jaya Malaysia Lovely Professional University Division of Research and Development Andhra Pradesh Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

SYN flood attacks pose a significant threat to the normal traffic flow in Mobile Ad Hoc Networks (MANETs). These attacks flood the network with unnecessary traffic, leading to congestion on specific routes. In this paper, we introduce an Adaptive Detection Mechanism that employs an Artificial Intelligence Technique. This method, known as SYN Flood Attack Detection Based on Bay Estimator (DsFaBe), is designed specifically for MANETs. Each node collects real-time information about the available channels. The DsFaBe then selects the most secure and congestion-free (Best Path) channel for traffic. Persistent congestion can lead to SYN Flood attacks. However, our use of AI techniques has resulted in a higher probability of detecting SYN Flood attacks compared to other methods. We also found that our proposed algorithm is cost-effective and more robust compared to existing approaches. The results of our simulations support our theory. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Research Progress On Name Data Networking To Achieve A Superior National Product In Indonesia 8

Research Progress On Name Data Networking To Achieve A Super...

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8th International Conference on Wireless and Telematics, ICWT 2022

作者： Syambas, Nana Rachmana Juhana, Tutun Hendrawan Mulyana, Eueung Edward, Ian Joseph Matheus Situmorang, Hamonangan Mayasari, Ratna Negara, Ridha Muldina Yovita, Leanna Vidya Wibowo, Tody Ariefianto Ahdan, Syaiful Nurkahfi, Galih Nugraha Nurhayati, Ade Mulya, Hafiz Budiana, Mochamad Soebagja Institute of Technology School of Electrical Engineering and Informatics Bandung Bandung Indonesia Telkom University School of Electrical Engineering Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung35132 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Faculty of Engineering Institut Teknologi Telkom Jakarta Jakarta11710 Indonesia

ISBN: (数字)9781665451222

ISBN: (纸本)9781665451222

Global traffic data are proliferating, including in Indonesia. The number of internet users in Indonesia reached 205 million in January 2022. This data means that 73.7% of Indonesia's population has used the internet. The median internet speed for mobile phones in Indonesia is 15.82 Mbps, while the median internet connection speed for Wi-Fi in Indonesia is 20.13 Mbps. As predicted by many, real-time traffic such as multimedia streaming dominates more than 79% of traffic on the internet network. This condition will be a severe challenge for the internet network, which is required to improve the Quality of Experience (QoE) for user mobility, such as reducing delay, data loss, and network costs. However, IP-based networks are no longer efficient at managing traffic. Named Data Network (NDN) is a promising technology for building an agile communication model that reduces delays through a distributed and adaptive name-based data delivery approach. NDN replaces the 'where' paradigm with the concept of 'what'. User requests are no longer directed to a specific IP address but to specific content. This paradigm causes responses to content requests to be served by a specific server and can also be served by the closest device to the requested data. NDN router has CS to cache the data, significantly reducing delays and improving the internet network's quality of Service (QoS). Motivated by this, in 2019, we began intensive research to achieve a national flagship product, an NDN router with different functions from ordinary IP routers. NDN routers have cache, forwarding, and routing functions that affect data security on name-based networks. Designing scalable NDN routers is a new challenge as NDN requires fast hierarchical name-based lookups, perpackage data field state updates, and large-scale forward tables. We have a research team that has conducted NDN research through simulation, emulation, and testbed approaches using virtual machines to get the best NDN router design be

关键词： Testbeds

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Enabling IoT Continuous Connectivity in Smart Spaces 22

Enabling IoT Continuous Connectivity in Smart Spaces

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22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Batalla, Jordi Mongay Dobre, Ciprian Markakis, Evangelos Mastorakis, George University of Nicosia University of Nicosia Research Foundation Departement of Computer Science Nicosia Cyprus Warsaw University of Technology Warsaw Poland University Politehnica of Bucharest National Institute for Research and Development in Informatics Automatic Control and Computers Bucharest Romania Hellenic Mediterranean University Department of Electrical and Computer Engineering Heraklion Greece Hellenic Mediterranean University Department of Management Science and Technology Agios Nikolaos Greece

ISBN: (纸本)9798350341270

Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area. © 2023 IEEE.

关键词： Genetic algorithms

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Analysis of Virtual Reality Based on the Internet of Things on Human Psychology ‘Internet of Thoughts’ (IoThs) for Rich Content Extraction Applied Natural Language Processing and Deep Learning 1

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20th International Conference on Smart Living and Public Health, ICOST 2023

作者： Mah, Pascal Muam Skalna, Iwona Pelech-Pilichowski, Tomasz Derlecki, Tomasz Nasr, Mahmoud Munyeshuri, Eric Amuzang, Gilly Njoh Itoe, Micheal Blake Somaah Tah, Ning Frida Department of Technical Informatics and Telecommunications AGH University of Science and Technology Krakow Poland Department of Business Informatics and Management Engineering AGH University of Science and Technology Krakow Poland Institute of Computer Science AGH University of Science and Technology Krakow Poland Department of Economics and Management Industry AGH University of Science and Technology Krakow Poland Department of Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Engineering Mathematics and Physics Faculty of Engineering and Technology Future University Cairo Egypt Department of Production Engineering and Logistics Enterprise Silesian University of Technology Gliwice Poland Department of Logistics Management Silesian University of Technology Gliwice Poland

ISBN: (数字)9783031439506

ISBN: (纸本)9783031439490

In the past decade, a lot of challenges to access, assess, and to acquire the needed technological opportunities to teach computers what naturally comes from the human brain and to understand how we naturally react when we rely on technology. The ability to document human thoughts, reactions and behavior to computers has led to the coming of NLP, AI, Dl, & ML. Aim to understand the influence of IoT on humans with the use of DL to achieve content correctness and accuracy with virtual technology. Studies show that the way we think, react, and do the things we think "Internet of thoughts" reflect our personality. The way we think determines the way we react and the way we do things are based on how we think. technology advancement has reinforced a lot of changes in humans which makes humans vulnerable to personal content exposure misappropriation due to the continuously changing nature of humanity and language. The study uses NLP, DL and behavior-oriented drive and influential function and results show that IoT based on VR influences human psychology "Internet of Thoughts". © 2023, The Author(s).

关键词： Internet of things

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DERAINIZER for Unpaired Single Image De-raining

DERAINIZER for Unpaired Single Image De-raining

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2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

作者： Fernando, Hasal Ayoob, Mohamed Poravi, Guhanathan University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computer Science Colombo 06 Sri Lanka

ISBN: (纸本)9781665425032

Rain streaks are known to cause many performance or accuracy reductions in many computer vision systems. The problem of obstructing rain streaks is addressed within the domain of 'De-raining'. De-raining is the process of removing rain streaks. It has two main sub-categories which are video de-raining and single image de-raining. Many works on single image de-raining have taken place using both traditional approaches as well as deep learning approaches. This paper does a critical analysis of credible, novel, and best performing single image de-raining systems. Also, the paper addresses an unpaired training gap which exists within the domain with the novel de-raining system 'DERAINIZER', enabling all future researchers to employ unpaired training data to train their models. It is followed by results, research limitations and possible future works. © 2021 IEEE.

关键词： computer vision

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Neural Network Controller Based Slippage Prevention System F or Electric Vehicle 3

Neural Network Controller Based Slippage Prevention System F...

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3rd IEEE KhPI Week on Advanced technology, KhPI Week 2022

作者： Vorobiov, Bohdan Senchenko, Serhii Pshenychnykov, Dmytro Brynza, Natalia Tymoshchenko, Andrii National Technical University «kharkiv Polytechnic Institute» Automated Electromechanical Systems Department Kharkiv Ukraine Simon Kuznets Kharkiv National University of Economics Department of Informatics and Computer Technology Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

Mathematical model of an asynchronous electric drive of an electric vehicle was built. The mathematical model of the mechanical part was built taking into account the possibility of modeling the slipping of each of the driving wheels separately with different coefficients of adhesion. A simplified model (plant model) was built, neural network parameters were chosen, such as the number of hidden layers, discretization, the number of samples and the number of epochs for training the neural network. The neural network was trained taking into account the linearized plant model, which reflects the qualitative form of real processes in the system. The analysis of the received results of network training has been carried out. The simulation of the system operation without slipping and with slipping of one of the wheels was carried out. The simulation results are compared with the mistake obtained during the training of the neural network, namely, the discrepancy between output and input signals. © 2022 IEEE.

关键词： Electric vehicles

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Mining Intelligence Hierarchical Feature for Malware Detection 15th

Mining Intelligence Hierarchical Feature for Malware Detecti...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Davuluri, Sandeep Kumar Soni, Mukesh ALMahadin, Ghayth Rivera, Richard Upadhyay, Jinal Patel, Pavan Department of Information Technology University of the Cumberlands Williamsburg United States Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Faculty of Information Technology Al Ahliyya Amman University Amman Jordan Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador HasmukhGoswami College of Engineering Gujarat Vahelal India Ahmedabad Institute of Technology Gujarat Ahmedabad India

ISBN: (纸本)9789819786305

In recent years, a new strain of malware, known as mining malware, has emerged, resulting in significant losses for its victims due to its encryption techniques. This research proposes an innovative approach to detect mining malware by integrating hierarchical threat intelligence features, focusing on the static attributes of mining malware. From the perspective of mining malware threat intelligence, we train classifiers using distinct layers of features, including byte-level features, Portable Executable (PE) structure features, and mining operation execution features. By leveraging diverse malicious software attributes for detection preferences, we develop a comprehensive mining malware detector based on a hierarchical feature integration methodology. To evaluate our approach, we conducted experiments in both controlled lab settings and real-world datasets. The results indicate that our hierarchical feature-integrated mining malware detection method achieves an impressive accuracy rate of 97.01% on the simulated real-world dataset, demonstrating a substantial 6.13% accuracy improvement compared to the baseline mining malware detection method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Data Driven Methods to Predict Vertical Profiles of the Rails: A Case Study

Data Driven Methods to Predict Vertical Profiles of the Rail...

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International Conference on ICT For Smart Society (ICISS)

作者： Ade Chandra Nugraha Suhono Harso Supangkat IGB Baskara Nugraha Yunendar Aryo Handoko Awan Hermawan Purwadinata Dimas Wisnu Saputro Department of Computer Engineering and Informatics Bandung State of Polytechnic School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Smart City and Community Innovation Center School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Mechanical and Aerospace Engineering Bandung Institute of Technology Bandung Indonesia Managing Director of Infrastructure PT Kereta Api Indonesia Bandung Indonesia Department of Computer Engineering and Informatics Bandung State of Polytechnic Bandung Indonesia

Predicting the value of railroad track geometry is needed to reduce the cost and time of rail condition measurements that have been carried out periodically every three months by railroad operators in Indonesia (PT Kereta Api Indonesia/PT KAI). Four track geometry parameters - vertical profile, horizontal alignment, twist, and gauge - are the reference measurements that will be mapped with alternative measurement results using accelerometers installed on regular trains (in-service trains). In this paper, eight prediction algorithms from the machine learning model are applied to map the vertical profile with 73 kilometers of measurement data in the Jakarta to Padalarang railroad segment, precisely on the Jatinegara-Cikampek measuring trip conducted from April 2019 to June 2022. The evaluation performance of the prediction results has been measured with the GadientBoosting Regressor algorithm is the best algorithm with MSE = 0.791, R^2 = 0.507 and feature importance on the Train speed and Az attributes. The research can be continued to detect the correlation of three other track geometry parameters referring to the dataset of rail geometry condition measurement results through accelerometers on regular trains (in-service trains).

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An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

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computers, Materials & Continua 2022年第10期73卷 493-511页

作者： Muhammad Zubair Rehman Muhammad Aamir Nazri Mohd.Nawi Abdullah Khan Saima Anwar Lashari Siyab Khan Faculty of Computing and Information Technology Sohar UniversitySohar311Sultanate of Oman Soft Computing&Data Mining Centre(SMC) Faculty of Computer Science&Information TechnologyUniversiti Tun Hussein Onn Malaysia(UTHM)Batu Pahat86400Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Institute of Computer Sciences and Information Technology The University of Agriculture25120PeshawarPakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

关键词： DNA sequence classification bat algorithm levy flight back propagation neural network hybrid artificial neural networks(HANN)

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