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16th International Conference on computer engineering and systems, ICCES 2021

作者： Soliman, Khaled Sobh, Mohamed Ali Bahaa-Eldin, Ayman M. Ain Shams University Dept. of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9781665408677

The development of cyber-attacks has unprecedented effect on businesses and governments. The recent years have witnessed various number of security breaches against organizations equipped by diverse cybersecurity solutions. These attacks showed the limitations of the current security systems to remediate the advanced threats. This critical challenge poses a vital need to find new techniques to detect the recently developed malwares called Zero-Day Threats. This type of attacks cannot be discovered by signature-based scanning and traditional cybersecurity systems. In addition, they can bypass the network security appliances by concealing malwares in the encrypted channels such as Virtual Private Network (VPN) and Hypertext Transfer Protocol Secure (HTTPS). Therefore, there is a great importance to study the endeavors of developing a Host-based Intrusion Detection System (HIDS) using Artificial Intelligence (AI) techniques to protect computers against sophisticated cyber-attacks. This paper will discuss and compare the recent proposed techniques using Machine Learning at the host level to detect the advanced cybersecurity attacks based on the behavioral impact of these attacks on computers. © 2021 IEEE.

关键词： Cybersecurity

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Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service

arXiv

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arXiv 2023年

作者： Zhang, Meiying Zhao, Huan Ebron, Sheldon Xie, Ruitao Yang, Kan Dept. of Computer Science University of Memphis United States College of Computer Science and Software Engineering Shenzhen University China

Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. However, for a given FL task, how to select a group of appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a multi-criteria client selection and scheduling scheme with a fairness guarantee, comprising two stages: 1) preliminary client pool selection, and 2) per-round client scheduling. Specifically, we first define a client selection metric informed by several criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. To guarantee fairness, we further formulate the per-round client scheduling problem and propose a heuristic algorithm to divide the client pool into several subsets such that every client is selected at least once while guaranteeing that the ‘integrated’ dataset in a subset is close to an independent and identical distribution (iid). Our experimental results show that our scheme can improve the model quality especially when data are non-iid. © 2023, CC BY.

关键词： Heuristic algorithms

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Selection of Beamforming in 5G MIMO scenarios using Machine Learning approach 19

Selection of Beamforming in 5G MIMO scenarios using Machine ...

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19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Silva, Douglas Henrique Augusto Ribeiro, David Ramirez, Miguel Arjona Lopes Rosa, Renata Chaudhary, Sushank Rodriguez, Demostenes Zegarra Federal University of Lavras Dept. of Computer Science MG Lavras Brazil Dept. of Automation Federal University of Lavras MG Lavras Brazil University of São Paulo Dept. of Electronic Systems Engineering São Paulo Brazil Chulalongkorn University Dept. of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

In new generation networks, 5G and 6G networks, intelligent mechanisms based on artificial intelligence algorithms are playing a relevant role in the performance improvement at different network levels. In 5G networks, different techniques are used, such as MIMO systems, and its network infrastructure is utilized by diverse services, for instance, vehicular communications. Thus, the description and the tracing of a communication scenario needs a large volume of data. Because of the difficulty to implement actual 5G networks, there is a lack of datasets containing complete 5G scenarios, the data is not enough or contain imbalanced classes to properly train machine learning (ML) algorithms. In this context, we propose a method to increase the amount of data to improve the machine learning performance of some classification models, specifically Random Forest, Multilayer Perceptron, and k-Nearest Neighbors. In the experimental results of the test phase, considering the inclusion of synthetic data, Random Forest, Multilayer Perceptron and k-Nearest Neighbors reached macro F1 scores of 0.9341, 0.9241 and 0.9456, respectively, which are superior to the results obtained when training with the original data only. © 2022 IEEE.

关键词： MIMO systems

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Realization of a Novel, Lightweight and Secured CoAP Based Monitoring System in Smart Logistics 6

Realization of a Novel, Lightweight and Secured CoAP Based M...

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6th International Multi-Topic ICT Conference, IMTIC 2021

作者： Hasan, Salva Ahmed, Salman Marwat, Safdar Nawaz Khan University of Engineering Technology Dept. of Computer Systems Engg Peshawar Pakistan

ISBN: (纸本)9781665482943

Internet of Things (IoT)-where the physical components are able to communicate with each other and with the internet-is one of the driving forces behind the Fourth Industrial Revolution. Nowadays, IoT applications have become widespread across many industries. However, due to the resource-constrained nature of IoT devices, they cannot be secured using conventional security protocols that are already developed. Hence, lightweight implementations of IoT security are the need of the hour. This paper proposes a secure and tamper-resistant system for monitoring the status of items in a container during transportation and storage using IoT devices. As smart logistics often transport items that are sensitive to environmental changes such as temperature and humidity, the security of data obtained from these sensors is the main focus of this research. To evaluate the performance of the system, a pilot-scale silo has been fabricated. Constrained Application Protocol (CoAP) is used as the communication protocol and a lightweight implementation of Datagram Transport Layer Security (DTLS) protocol has been adapted for the smart container architecture to incorporate cryptographic security. The low power consumption of the lightweight DTLS-secured CoAP implementation will drastically increase the efficiency of the smart logistics architecture without compromising its security. © 2021 IEEE.

关键词： Internet of things

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Corpus of Human-Labeled Indirect Harassment

Corpus of Human-Labeled Indirect Harassment

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International Conference on Emerging Technologies, ICET

作者： Ebtihaj Abdullah Waseem Ullah Khan Safdar Nawaz Khan Marwat Salman Ahmed Dept. of Computer Systems Engg University of Engineering & Technology Peshawar Pakistan Faculty of Computer Science and Engg GIK Institute of Engineering Sciences & Technology Swabi Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

In an age of information and digital communication, social media platforms are becoming essential spaces where individuals can voice their thoughts, exchange ideas, and take part in discussions on a variety of subjects. High quality and high value datasets of such ideas and discussions in textual and formatted form are fundamental to the research aspects of Artificial Intelligence and data-science. In this paper, we present a dataset of indirect harassment. There are approximately 10,700 tweets with binary labels. The labels were assigned by a team of researchers collectively. The dataset contains approximately 19 percent positive indirect harassment labels and 81 percent negative indirect harassment labels. The data is useful for training and running machine and deep learning models to detect indirect and direct harassment. The ease in understanding the data for researchers and other peers is also very crucial. The corpus mentioned in this paper is easy to understand via the use of a binary label. The data is simplistic in nature because there are only two columns with one being the text and the other being the indirect label. This work was necessary because it was a component of a larger project that also needed this kind of dataset.

关键词： Deep learning Training Social networking (online) Oral communication Data science Digital communication Information age Data models

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Improving Gamma Imaging in Proton Therapy by Sanitizing Compton Camera Simulated Patient Data using Neural Networks through the BRIDE Pipeline

Improving Gamma Imaging in Proton Therapy by Sanitizing Comp...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Michael O. Hodge, Julian Jin, Peter L. Protz, Ella Wong, Elizabeth Obe, Ruth Shakeri, Ehsan Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Sharma, Vijay R. Mossahebi, Sina Ren, Lei Peterson, Stephen W. Polf, Jerimy C. Dartmouth College Departments of Mathematics United States U. of Maryland Dept. of Mathematics and Statistics United States James M. Bennett High School SalisburyMD United States Florida Atlantic University Department of Mathematics and Sciences United States Brookdale Community College Department of Mathematics United States U. of Houston Dept. of Computer Science and of Software Engineering Clear Lake United States U. of Maryland Dept. of Information Systems United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa M3D Inc. United States

ISBN: (纸本)9798350362480

Precision medicine in cancer treatment increasingly relies on advanced radiotherapies, such as proton beam radiotherapy, to enhance efficacy of the treatment. When the proton beam in this treatment interacts with patient matter, the excited nuclei may emit prompt gamma ray interactions that can be captured by a Compton camera. The image reconstruction from this captured data faces the issue of mischaracterizing the sequences of incoming scattering events, leading to excessive background noise. To address this problem, several machine learning models such as Feedfoward Neural Networks (FNN) and Recurrent Neural Networks (RNN) were developed in PyTorch to properly characterize the scattering sequences on simulated datasets, including newly-created patient medium data, which were generated by using a pipeline comprised of the GEANT4 and Monte-Carlo Detector Effects (MCDE) softwares. These models were implemented using the novel 'Big-data REU Integrated Development and Experimentation' (BRIDE) platform, a modular pipeline that streamlines preprocessing, feature engineering, and model development and evaluation on parallelized GPU processors. Hyperparameter studies were done on the novel patient data as well as on water phantom datasets used during previous research. Patient data was more difficult than water phantom data to classify for both FNN and RNN models. FNN models had higher accuracy on patient medium data but lower accuracy on water phantom data when compared to RNN models. Previous results on several different datasets were reproduced on BRIDE and multiple new models achieved greater performance than in previous research. © 2024 IEEE.

关键词： Phantoms

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Physics-Based Spatio-Temporal Modeling With Machine Learning for the Prediction of Oceanic Internal Waves 19

Physics-Based Spatio-Temporal Modeling With Machine Learning...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Wu, Song Zhang, Xiaojiang Dong, Wei Wang, Senzhang Li, Xiaoyong Bao, SenLiang Li, Kai National University of Defense Technology Dept. College of Computer Science and Technology Changsha China National University of Defense Technology Dept. College of Meteorology and Oceanography Changsha China Central South University School of Computer Science and Engineering Changsha China National University of Defense Technology Dept. College of Systems Engineering Changsha China

ISBN: (纸本)9798350346558

Accurately predicting the occurrence of oceanic internal waves in the northeastern South China Sea is of great importance to marine ecosystems, and economy. The traditional physics-based models for monitoring the occurrence of internal waves require complex parameterization, and the partial differential equations (PDEs) are relatively difficult to solve. The emergence of integrating physical knowledge and data-driven models brings light to solving the problem, which improves interpretability and meets the physical consistency. It not only inherits the advantages of machine learning in massive data processing but also makes up for the 'black box' characteristics. In this paper, we propose a physics-based spatio-temporal data analysis model based on the widely used LSTM framework to achieve oceanic internal wave prediction. The results show higher prediction accuracy compared with the traditional LSTM model, and the introduction of physical laws can improve data utilization while enhancing interpretability. © 2022 IEEE.

关键词： Forecasting

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Work in Progress: Congestion Control in mmWave Fluctuating Scenarios in 5G-A/6G

Work in Progress: Congestion Control in mmWave Fluctuating S...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Jorge Ignacio Sandoval Sandra Céspedes Department of Electrical Engineering University of Chile Santiago Chile Dept. of Computer Science & Software Eng Concordia University Montreal Canada

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The development of the next generation of cellular networks, 5G-Advanced and 6G (5G-A/6G), offers higher speeds, sub-millisecond latency, and providing wider coverage, which are key to meeting the requirements of new and demanding applications. As such, there are several challenges in managing and leveraging this opportunity to ensure that channel conditions do not impact the application due to mobility and obstructions. 5G introduces the use of millimeter wave (mmWave), and the commercial deployment of this technology brings to light the issues related to propagation and mobility in high-frequency bands, and their impact on applications has begun to be studied by exploring the response of the congestion control algorithms (CCAs) in representative scenarios. However, the highly variable channel conditions in mmWave require that they be defined as fluctuating bandwidth scenarios. It is necessary to determine how the transport layer can take advantage of the maximum available capacity and not affect the applications, and whether the network promises are fulfilled. This work aims to design and evaluate a CCA capable of efficiently adapting to the fluctuating bandwidth of extreme condition scenarios of 5G-A/6G mobile networks and to compare it with the state-of-the-art algorithms. As a first result, a set of simulation-based evaluation using 5GLENA was discussed. A combination of CCAs and different radio link control (RLC) buffer sizes allows us to highlight the importance of defining appropriate metrics and including convergence times as part of the evaluation performance of the algorithms. Algorithms such as HighSpeed and New Reno are among the slowest to converge, while BBR is the fastest. The analysis and characterization of the algorithms will allow us to define the requirements for designing an Machine Learning (ML) based algorithm with optimal performance and better convergence times.

关键词： Measurement Machine learning algorithms Millimeter wave technology Bandwidth Machine learning Radio links Millimeter wave propagation Millimeter wave communication Next generation networking Convergence

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An Automated Wavelet Generation Tool for Cyberattack Detection in Substation Automation systems

An Automated Wavelet Generation Tool for Cyberattack Detecti...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Matthew Oinonen Walid G. Morsi Computer and Software Engineering Dept. Smart Grid & Electric Vehicles Research Laboratory Electrical Ontario Tech University Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

This paper presents a novel method, the Automated Custom Wavelet Generation-Deep Learning-based classifier (ACWGDL), for cyberattack detection in smart grids. The proposed method generates custom wavelets for enhanced feature extraction of cyberattacks on substation automation systems (SASs). Physical and network data from a substation is converted into time-frequency spectrograms using the Discrete Wavelet Transform (DWT) with the custom generated wavelets. Deep learning is implemented with a convolutional neural network to classify the generated spectrograms as cyberattacks from normal cases and power quality disturbance cases. The proposed method has been tested on two publicly available datasets from two different substation automation systems. The results have shown that the use of the custom wavelets was able to enhance the detection accuracy of the cyberattacks by up to 11% compared to the existing benchmark wavelets. The proposed method is versatile and has demonstrated its effectiveness in enhancing the detection accuracy of the cyberattacks irrespective of the choice of the datasets.

关键词： Training Time-frequency analysis Substation automation Accuracy Benchmark testing Feature extraction Discrete wavelet transforms

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Towards an integration management maturity

Towards an integration management maturity

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Sonja M. Hyrynsalmi Helena Holmström Olsson Jan Bosch Dept. Software Engineering LUT University Lahti Finland Dept. Computer Science and Media Technology Malmö University Malmö Sweden Dept. Computer Science and Engineering Chalmers University of Technology Göteborg Sweden

As industries continue to digitize at a rapid pace, the number of integrations, the connections between different digital systems, is increasing. As a consequence, integration management is becoming increasingly difficult, leading to scalability, stability, and system outage problems that directly affect company performance. For that reason the modern integration management platforms have emerged to offer efficient management of the growing number of integrations, as well as a new way to handle, enrich, and utilize data both inside and outside *** research involved interviews with 20 software vendor professionals and 20 clients who had recently undergone an integration platform project. The study identified key drivers behind integration management projects and recognized different maturity levels in integration management. The results revealed that more mature companies are utilizing modern integration platforms to support their digital transformation. The study also highlighted the growing importance of data management and utilization in integration *** address these findings, a maturity model was developed to help companies evaluate their current integration management status and find ways to progress in their integration management.

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