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Cyber Security in Networking Conference (CSNet)

作者： Mohamad Wazzeh Mohamad Arafeh Hakima Ould-Slimane Chamseddine Talhi Azzam Mourad Hadi Otrok Department of Software and IT Engineering Ecole de Technologie Supérieure (ÉTS) Montreal Canada Department of Mathematics and Computer Science Université du Québec à Trois-Riviéres (UQTR) Trois-Rivières Canada Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security.

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Fault-tolerant control of semi-active suspension in case of oil leakage of magnetorheological damper

Fault-tolerant control of semi-active suspension in case of ...

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European Control Conference (ECC)

作者： Hakan Basargan Filip Jeniš András Mihály Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2 Budapest Hungary Faculty of Mechanical Engineering Institute of Machine and Industrial Designs Brno University of Technology Technicka 2 Brnos Czech Republic Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende u. 13-17 Budapest Hungary

The paper presents a reconfigurable fault-tolerant control strategy for a semi-active suspension using magnetorheological (MR) damper. The aim of the control reconfiguration is to handle the adverse behaviour of the MR damper due to oil leakage induced by the wear of the suspension component. The proposed method relies on the data driven model of the MR damper, using an estimation procedure to quantify the healthiness of the damper and to estimate the performance degradation due to the oil leakage. The reconfiguration control strategy is founded on the Linear Parameter Varying (LPV) framework, where a scheduling variable is defined to represent the healthiness level of the MR damper. By the scaling of the control action through the scheduling variable, the performance degradation of the MR damper can be compensated to match the behaviour of the healthy dampers. The proposed method is demonstrated through simulations, comparing the performance of the fault-tolerant LPV control to conventional semi-active control methods

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Safe trajectory design for indoor drones using reinforcement-learning-based methods

Safe trajectory design for indoor drones using reinforcement...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Dénes Tompos Balázs Németh Eötvös Loránd Research Network (ELKH) Kende utca 13-17. Institute for Computer Science and Control (SZTAKI) Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2. Budapest Hungary

This paper proposes a design method for achieving safe trajectory of indoor drones. The trajectory design with a reinforcement-learning-based (RL) agent is facilitated, which can result in efficient and collision-free motion. The method is developed for motion in indoor area with moving mobile robots, and thus, the collision with these obstacles must be avoided. Through RL-based design the fast motion of the drones can be achieved, which must perform a mission between workstations in a manufacturing system. The effectiveness of the design process through a simulation example on a real laboratory environment is illustrated.

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Modeling Digital Twins of Kubernetes-Based Applications

Modeling Digital Twins of Kubernetes-Based Applications

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IEEE Symposium on computers and Communications (ISCC)

作者： Davide Borsatti Walter Cerroni Luca Foschini Genady Ya. Grabarnik Filippo Poltronieri Domenico Scotece Larisa Shwartz Cesare Stefanelli Mauro Tortonesi Mattia Zaccarini University of Bologna Bologna Italy Department of Mathematics and Computer Science St. John's University Queens NY USA Distributed Systems Research Group University of Ferrara Ferrara Italy IBM TJ W2atson Research Center Operational Innovation NY USA

Kubernetes provides several functions that can help service providers to deal with the management of complex container-based applications. However, most of these functions need a time-consuming and costly customization process to address service-specific requirements. The adoption of Digital Twin (DT) solutions can ease the configuration process by enabling the evaluation of multiple configurations and custom policies by means of simulation-based what-if scenario analysis. To facilitate this process, this paper proposes KubeTwin, a framework to enable the definition and evaluation of DTs of Kubernetes applications. Specifically, this work presents an innovative simulation-based inference approach to define accurate DT models for a Kubernetes environment. We experimentally validate the proposed solution by implementing a DT model of an image recognition application that we tested under different conditions to verify the accuracy of the DT model. The soundness of these results demonstrates the validity of the KubeTwin approach and calls for further investigation.

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User-Centric Interactive AI for Distributed Diffusion Model-based AI-Generated Content

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Chinese University of Hong Kong Shenzhen China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Distributed Artificial Intelligence-Generated Content (AIGC) has attracted increasing attention. However, it faces two significant challenges: how to maximize the subjective Quality of Experience (QoE) and how to enhance the energy efficiency, which are particularly pronounced in widely adopted Generative Diffusion Model (GDM)-based AIGC services for image generation. In this paper, we propose a novel user-centric Interactive AI (IAI) approach for service management, with a distributed GDM-based AIGC framework, prioritizing efficient and collaborative GDM deployment. Specifically, we restructure the GDM's inference process, i.e., the denoising chain, to enable users' semantically similar prompts to share a portion of diffusion steps. Furthermore, to maximize the users' subjective QoE, we propose a IAI approach, i.e., Reinforcement Learning With Large Language Models Interaction (RLLI), which utilizes Large Language Model (LLM)-empowered generative agents to replicate users interaction, providing real-time and subjective QoE feedback that reflects a spectrum of user personalities. Lastly, we present the GDM-based Deep Deterministic Policy Gradient (G-DDPG) algorithm, adapted to the proposed RLLI framework, for effective communication and computing resource allocation while considering user subjective personalities and dynamic wireless environments in decision-making. Simulation results show that G-DDPG can increase the sum QoE by 15%, compared with the conventional DDPG algorithm. © 2023, CC BY.

关键词： Reinforcement learning

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Review of Approaches to the Use of UAVs, Remote Sensing and GIS in Humanitarian Demining: Ukrainian Case

SSRN

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SSRN 2024年

作者： Hutsul, Taras Khobzei, Mykola Tkach, Vladyslav Krulikovskyi, Oleh Moisiuk, Oleksandr Ivashko, Victor Samila, Andrii Department of Geomatics Land and Agromanagement Yuriy Fedkovych Chernivtsi National University Kotsyubynsky 2 Chernivtsi58002 Ukraine Department of Radio Engineering and Information Security Yuriy Fedkovych Chernivtsi National University Kotsyubynsky 2 Chernivtsi58002 Ukraine Department of Computer Sciences Yuriy Fedkovych Chernivtsi National University Kotsyubynsky 2 Chernivtsi58002 Ukraine Integrated Center for Research Development and Innovation in Advanced Materials Nanotechnologies and Distributed Systems for Fabrication and Control Stefan cel Mare University of Suceava Street University 13 Suceava720229 Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Street University 13 Suceava720229 Romania

The history of the use of mines dates back almost two centuries. The geography of their use and the associated social harm have made them, without exaggeration, a global problem. At the same time, searches were underway for safe methods of their neutralization using various technical means. In so doing, until now, none of the existing methods provides a 100% guarantee of cleaning the territory, which determines the purpose of finding innovative methods and the possibility of combining them with existing ones. Unmanned aerial vehicles (UAVs) are becoming a real modern breakthrough in the field of intellectual achievements. Obtaining optimal results when solving a wide range of different problems, together with the development of composite materials, software, and the latest navigation equipment, make the tasks assigned to them and the expected results more and more difficult. UAVs allow people not to be in life-threatening conditions, to conduct activities beyond their physiological and psychophysiological abilities. The combination of the ability to collect spatial data during flight in various ranges of remote sensing with the possibility of carrying variants of useful equipment opens up prospects for their use in the field of demining territories. Supplementing UAV technologies with modern information systems for processing and analysis of information (expert systems, machine learning, computational intelligence, distributed artificial intelligence, neural networks, etc.), including spatial (GIS), opens up great prospects in the field of humanitarian demining of territories. © 2024, The Authors. All rights reserved.

关键词： Geographic information systems

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Towards Bilateral Client Selection in Federated Learning Using Matching Game Theory

Towards Bilateral Client Selection in Federated Learning Usi...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Osama Wehbi Sarhad Arisdakessian Omar Abdel Wahab Hadi Otrok Safa Otoum Azzam Mourad Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Computer Science & Engineering Université du Québec en Outaouais Gatineau Canada Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE College of Technological Innovation Zayed University Dubai UAE Division of Science New York University Abu Dhabi UAE

ISBN: (纸本)9781665435413

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several devices. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality and resources across the participants. To overcome this problem, we propose an intelligent client selection approach for federated learning on IoT devices using matching game theory. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several criteria such as accuracy and price, and (2) intelligent matching algorithms that take into account the preferences of both parties in their design. Based on our simulation findings, our strategy surpasses the VanillaFL selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model.

关键词： Training Federated learning Simulation Data integrity Collaboration Games Servers

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GENERATIVE CAUSAL REPRESENTATION LEARNING FOR OUT-OF-DISTRIBUTION MOTION FORECASTING

arXiv

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arXiv 2023年

作者： Bagi, Shayan Shirahmad Gale Gharaee, Zahra Schulte, Oliver Crowley, Mark Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada

Conventional supervised learning methods typically assume i.i.d samples and are found to be sensitive to out-of-distribution (OOD) data. We propose Generative Causal Representation Learning (GCRL) which leverages causality to facilitate knowledge transfer under distribution shifts. While we evaluate the effectiveness of our proposed method in human trajectory prediction models, GCRL can be applied to other domains as well. First, we propose a novel causal model that explains the generative factors in motion forecasting datasets using features that are common across all environments and with features that are specific to each environment. Selection variables are used to determine which parts of the model can be directly transferred to a new environment without fine-tuning. Second, we propose an end-to-end variational learning paradigm to learn the causal mechanisms that generate observations from features. GCRL is supported by strong theoretical results that imply identifiability of the causal model under certain assumptions. Experimental results on synthetic and real-world motion forecasting datasets show the robustness and effectiveness of our proposed method for knowledge transfer under zero-shot and low-shot settings by substantially outperforming the prior motion forecasting models on out-of-distribution prediction. Our code is available at https://***/sshirahmad/GCRL. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Model of the System for Special Purpose of Critical Infrastructure Objects 3

Model of the System for Special Purpose of Critical Infrastr...

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3rd International Scientific and Practical Conference on Information Security and Information Technologies, ISecIT 2021

作者： Karpinski, Mikolaj Tomashevsky, Bogdan Zahorodna, Natalia Yevseiev, Serhii Rajba, Stanislaw Milov, Oleksandr University of Bielsko-Biala Department of Computer Science and Automatics Willowa Str. 2 Bielsko-Biala43-309 Poland Ternopil Ivan Puluj National Technical University Department of Cyber Security Ruska str. 56 Ternopil46001 Ukraine Simon Kuznets Kharkiv National University of Economics Cybersecurity and Information Systems Department ave. Science 9-A Kharkiv61166 Ukraine

The rapid development of computing, mobile and Internet technologies, the digital economy, on the one hand, hybridity and synergy, the development of post-quantum cryptography (the emergence of a full-scale quantum computer), on the other, put forward more stringent requirements for the principles of building special security mechanisms in modern specialpurpose systems. Targeted attacks in cyberspace also require a change not only in the principles of building a special communication system for critical infrastructure objects (SCS CIO), the system for communicating commands / control signals to the CIO elements, as well as the creation of fundamentally new approaches to the formation and transmission of commands for their use not only of the SCS equipment, as well as open modern commercial systems based on Internet technologies. This approach allows, in the context of the economic crisis, to ensure the delivery of the signal within a certain time frame in the conditions of modern hybrid cyber threats to the control system through the use of cyberspace infrastructure (synthesis of modern technologies of computer systems and networks, Internet technologies and technologies of mobile communication). The proposed mathematical component of the assessment of the reliability and probability of delivering the corresponding commands / signals allows the proposed model to be used to simulate various interventions into a special-purpose system, both external and internal. ©? 2021 Copyright for this paper by its authors.

关键词： Critical infrastructures

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Fractional heterogeneous telegraph processes: Interplay between heterogeneity, memory, and stochastic resetting

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Physical Review E 2024年第2期110卷 024101-024101页

作者： Trifce Sandev Alexander Iomin Research Center for Computer Science and Information Technologies <a href="https://***/003jsdw96">Macedonian Academy of Sciences and Arts</a> Bul. Krste Misirkov 2 1000 Skopje Macedonia Institute of Physics Faculty of Natural Sciences and Mathematics <a href="https://***/02wk2vx54">Ss. Cyril and Methodius University</a> Arhimedova 3 1000 Skopje Macedonia and Department of Physics Korea University Seoul 02841 Korea. Solid State Institute <a href="https://***/03qryx823">Technion</a> Haifa 32000 Israel and <a href="https://***/01bf9rw71">Max Planck Institute for the Physics of Complex Systems</a> Dresden Germany.

Fractional heterogeneous telegraph processes are considered in the framework of telegrapher's equations accompanied by memory effects. The integral decomposition method is developed for the rigorous treating of the problem. Exact solutions for the probability density functions and the mean squared displacements are obtained. A relation between the fractional heterogeneous telegrapher's equation and the corresponding Langevin equation has been established in the framework of the developed subordination approach. The telegraph process in the presence of stochastic resetting has been studied, as well. An exact expression for both the nonequilibrium stationary distributions/states and the mean squared displacements are obtained.

关键词： Diffusion Fluctuations & noise Stochastic processes Stochastic resetting

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