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检索条件"机构=Distributed Systems Group in the Department of Computer Science"

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Data Fusion-based Hybrid Framework for Cyber-physical Security of the Smart Grid

Data Fusion-based Hybrid Framework for Cyber-physical Securi...

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2024 Resilience Week, RWS 2024

作者： Boamah, Sharon A. Mathieu, Reynold Faillace, Victor Agnew, Dennis McNair, Janise Bretas, Arturo University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States Sandia National Laboratory Distributed Systems Group United States

ISBN: (纸本)9798350388985

The integration of information communication technology with the power grid exposes it to cyber threats. The network state estimation process provides stability to the smart grid. The communication network plays a major role in ensuring the successful transmission of state information. However, these network measurements are vulnerable to malicious attacks. This subsequently affects the network measurement such as associated high transmission delays and packet losses affecting the reliability of the smart grid. In this work, we propose a hybrid physics-based data-driven model that uses data fusion from the state-of-the-art physics-based Network State Estimation model and a data-driven model to detect false data injection attacks in the communication network layer of the smart grid. The performance of the data fusion method is evaluated and the simulation results show that the proposed model outperforms the standalone approaches in the detection of bad data. This shows that the proposed scheme is able to improve the cyber-physical security of the communication network layer of the smart grid. © 2024 IEEE.

关键词： Cyber attacks

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Unveiling the Black Box: Neural Cryptanalysis with XAI

Unveiling the Black Box: Neural Cryptanalysis with XAI

引用

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Goi, Yue-Tian Leong, Shu-Min Phan, Raphael C.-W. Lai, Shangqi Salagean, Ana School of Information Technology Monash University Australia CSIRO's Data61 Distributed Systems Security Group Australia Loughborough University Department of Computer Science United Kingdom

ISBN: (纸本)9781665410205

At CRYPTO'19, Gohr[1] presented ResNet-based neural distinguishers (ND) for the round-reduced SPECK32/64 cipher. However, due to the black-box use of such deep learning models, it is hard for humans to understand why these distinguishers work, impeding advancements in cryptanalytic knowledge. In this work, we aim to effectively adapt eXplainable Artificial Intelligence (XAI) techniques, notably Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to gain a detailed understanding of the important features useful in Gohr's neural distinguishers. © 2024 IEEE.

关键词： Ciphertext

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Data-driven modeling of hysteresis in ReRAM devices: A multifactorial approach for enhanced memory performance 35

Data-driven modeling of hysteresis in ReRAM devices: A multi...

引用

35th GI-Workshop on Foundations of Database systems, GvDB 2024

作者： Zamankhani, Saba TU Ilmenau Department of Computer Science and Automation Databases and Information Systems Group Germany

Many physical systems in the real world exhibit complex behavior, making it difficult to identify their dynamics. To overcome this problem, prototypes are created to provide a better insight into the behavior of such systems. However, building these prototypes using traditional methods is often slow due to the computational intensity required to accurately capture the detailed complexities within these systems and the large parameter space that needs to be explored. Data-driven approaches, such as machine learning and deep learning frameworks, have the potential to significantly speed up this process by generalizing the model. In this study, we explore the use of such an approach to understand the complex and non-linear behavior of dynamical systems, using resistive random access memory (ReRAM) devices as a case study. It is important to emphasize that modeling ReRAM is only one specific example in our wider investigation, which aims to design predictive models for a range of non-linear dynamical systems. Our work attempts to overcome the limitations of traditional research and development by using neural networks to reproduce the complex behavior of ReRAM cells accurately. We introduce a hybrid dual-input neural network architecture (HDiNN), equipped with a custom loss function, to capture both spatial and temporal patterns, improving the predictability of cell behavior under different conditions. This involves integrating important factors such as material properties, device geometry, and electrical interactions into our model to explain the complexities of ReRAM technology. However, our ambitions extend far beyond ReRAM to develop methods to help create innovative, durable solutions in various fields. This study highlights the impact of predictive modeling in advancing materials science and demonstrates the transformative potential of neural networks in improving the design and optimization of future technologies. © 2024 Copyright for this paper by its authors.

关键词： Neural networks

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Utilization of Database Spatial Queries for Region of Interest Extraction in Stored Images

Utilization of Database Spatial Queries for Region of Intere...

引用

2022 IEEE Region 10 International Conference, TENCON 2022

作者： Valdez, Julian Troy C. Tan, Wilson M. University of the Philippines Diliman Department of Computer Science Networks and Distributed Systems Group Quezon City Philippines

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Image processing, specifically extracting regions of interest, being a costly operation would benefit from a method of storing its output region and input image data in a database for future querying so as to prevent re-execution of the processing operation. This study investigates the performance of using built-in spatial queries found in a couple of modern Database systems to store and retrieve specific regions of image data. After testing two widely used Database software: MongoDB and Postgresql, results have shown that MongoDB performs better in terms of average and tail latency as well as efficiency in disk space consumption. © 2022 IEEE.

关键词： Query processing

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A Simulative Approach to AMR Fleet Sizing in Decentralized Multi-Robot Task Allocation 28

A Simulative Approach to AMR Fleet Sizing in Decentralized M...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Gödeke, Jana Detzner, Peter Fraunhofer Institute for Material Flow and Logistics Department for AI and Autonomous Systems Dortmund Germany Ruhr University Bochum Faculty of Computer Science Distributed and Networked Systems Group Bochum Germany

ISBN: (纸本)9798350339918

As the Industry 4.0 shifts towards the adoption of autonomous mobile robots (AMRs) in warehouses, decentralized decision-making has become a key design principle. Multi-robot task allocation (MRTA) is a problem that involves assigning tasks to AMRs while optimizing the performance of the system. However, modeling decentralized MRTA applications for optimization without a central instance poses significant challenges due to the autonomy and flexibility of AMRs. In this paper, we propose a simulative approach to address the fleet sizing problem combined with decentralized MRTA applications for AMRs. Based on simulation data, models have been developed that predict key performance indicators (KPIs) for different warehouse layouts and requirements, using techniques from machine learning and mathematical optimization. The model represents KPIs such as constraint satisfaction and utilization rates in a decentralized MRTA scenario including a self-organizing material flow application. Based on this model, we introduce a fleet size selection mechanism. This research contributes to the field of Industry 4.0 by providing a generalizable simulative approach that is adaptable to flexible warehouse environments, allowing for the application of any MRTA algorithm. Moreover, this approach allows the integration of different KPIs, facilitating the adaptation of requirements. © 2023 IEEE.

关键词： Warehouses

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SCOPE: A Synthetic Multi-Modal Dataset for Collective Perception Including Physical-Correct Weather Conditions 27

SCOPE: A Synthetic Multi-Modal Dataset for Collective Percep...

引用

27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Gamerdinger, Jorg Teufel, Sven Schulz, Patrick Amann, Stephan Kirchner, Jan-Patrick Bringmann, Oliver University of Tübingen Embedded Systems Group Faculty of Science Department of Computer Science Germany Karlsruhe Germany

ISBN: (纸本)9798331505929

Collective perception has received considerable attention as a promising approach to overcome occlusions and limited sensing ranges of vehicle-local perception in autonomous driving. In order to develop and test novel collective perception technologies, appropriate datasets are required. These datasets must include not only different environmental conditions, as they strongly influence the perception capabilities, but also a wide range of scenarios with different road users as well as realistic sensor models. Therefore, we propose the Synthetic COllective PErception (SCOPE) dataset. SCOPE is the first synthetic multi-modal dataset that incorporates realistic camera and LiDAR models as well as parameterized and physically accurate weather simulations for both sensor types. The dataset contains 17,600 frames from over 40 diverse scenarios with up to 24 collaborative agents, infrastructure sensors, and passive traffic, including cyclists and pedestrians. In addition, recordings from two novel digital-twin maps from Karlsruhe and Tübingen are included. The dataset is available at https://***/scope © 2024 IEEE.

关键词： Digital elevation model

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Rethinking Serverless Computing: from the Programming Model to the Platform Design 49

Rethinking Serverless Computing: from the Programming Model ...

引用

Joint Workshops at the 49th International Conference on Very Large Data Bases, VLDBW 2023

作者： Alonso, Gustavo Klimovic, Ana Kuchler, Tom Wawrzoniak, Michael Systems Group Computer Science Department ETH Zürich Switzerland

Serverless computing offers a number of advantages over conventional, Virtual Machine (VM) based deployments on the cloud, e.g., greater elasticity, simplicity of use and management, finer granularity billing, and rapid deployment and start up times. Naturally, there is a growing interest in exploring how to run applications in this new environment and data analytics is not an exception. Unfortunately, current serverless platforms are limited along several dimensions, which makes things quite difficult from the perspective of data analytics. In this paper we explore what serverless has to offer today, what is missing, and what can be done to make serverless a better computing platform in general and for data analytics in particular. © 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： Data Analytics

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Data Processing with FPGAs on Modern Architectures 23

Data Processing with FPGAs on Modern Architectures

引用

2023 ACM/SIGMOD International Conference on Management of Data, SIGMOD 2023

作者： Jiang, Wenqi Korolija, Dario Alonso, Gustavo Systems Group Department of Computer Science ETH Zürich Switzerland

ISBN: (纸本)9781450395076

Trends in hardware, the prevalence of the cloud, and the rise of highly demanding applications have ushered an era of specialization that is quickly changing the way data is processed at scale. These changes are likely to continue and accelerate in the next years as new technologies are adopted and deployed: smart NICs, smart storage, smart memory, disaggregated storage, disaggregated memory, specialized accelerators (GPUS, TPUs, FPGAs), as well as a wealth of ASICS specifically created to deal with computationally expensive tasks (e.g., cryptography or compression). In this tutorial we focus on data processing on FPGAs, a technology that has received less attention than, e.g., TPUs or GPUs but that is, however, increasingly being deployed in the cloud for data processing tasks due to the architectural flexibility of FPGAs and their ability to process data at line rate, something not possible with other type of processors or accelerators. In the tutorial we will cover what are FPGAs, their characteristics, their advantages and disadvantages over other design options, as well as examples from deployments in industry and how they are used in a variety of data processing tasks. Then we will provide a brief introduction to FPGA programming with High Level Synthesis (HLS) tools as well as briefly describe resources available to researchers in the form of academic clusters and open source systems that simplify the first steps. The tutorial will also include several case studies borrowed from research done in collaboration with companies that illustrate both the potential of FPGAs in data processing but also how software and hardware architectures are evolving to take advantage of the possibilities offered by FPGAs. © 2023 ACM.

关键词： Field programmable gate arrays (FPGA)

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Int-BEB: Enhanced Binary Exponential Backoff using Q-learning 5

Int-BEB: Enhanced Binary Exponential Backoff using Q-learnin...

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5th International Conference on Pattern Analysis and Intelligent systems, PAIS 2023

作者： Zerguine, N. Ferhat Abbas Setif 1 University Networks and Distributed Systems Laboratory Department of Computer Science Setif Algeria

ISBN: (纸本)9798350381450

Because of their development and success, MANETs are now preferred alternatives in many fields of study and application. The IEEE 802.11 standard is the basis of most wireless technologies. To treat collisions in wireless networks, IEEE 802.11 distributed Coordination Function (DCF) MAC layer employs Binary Exponential Backoff (BEB) algorithm, that is helpful in decreasing the potential of collision, but comes at the expense of several network performance metrics, such as medium access delay, bandwidth management, power use, and fairness. A Deep Learning technique called Deep Reinforcement Learning (DRL) allows an agent to communicate with the environment in order to accomplish a goal. In the present paper, we suggest Q-learning (QL), one of the DRL approaches to improve the Binary Exponential Backoff (BEB) in MANETs. The intelligently designed Int-BEB aims for better CW selection;this selection strikes to reduce collisions as much as possible and to give more chances to nodes that make collisions to access the channel by taking into consideration their residual energy. In-depth simulations are performed to evaluate the performance of the proposed mechanism. The performance of Int-BEB is evaluated in diverse networks with several criteria such as packet successful transmission, packet loss and energy. © 2023 IEEE.

关键词： Reinforcement learning

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Data Flow Architectures for Data Processing on Modern Hardware 40

Data Flow Architectures for Data Processing on Modern Hardwa...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Lerner, Alberto Alonso, Gustavo EXascale Infolab University of Fribourg Switzerland Systems Group Department of Computer Science ETH Zurich Switzerland

ISBN: (纸本)9798350317152

The requirements arising from ever growing amounts of data and tight performance constraints as well as the limitations encountered in improving conventional CPU performance have led to a proliferation of specialized architectures involving a wide variety of processor types (GPU, TPU, DPU, etc.) with processing becoming distributed across all points of the computing fabric (smart storage, smart memory, smart NICs, programmable switches, etc.). Examples abound both in industry and academia of new architectural configurations and hardware accelerators improving different aspects of a system. These developments raise an important question that is still open but has not attracted sufficient attention: how to design data processing engines systems over such highly heterogeneous and distributed architectures. In this paper we argue that data management engines on modern hardware will necessarily be based on data flow designs where processing happens in a streaming and pipelined fashion across the entire architecture, a radical departure from existing engines. In the paper we argue why this will be the case, the advantages of such designs, and outline a research program to allow data processing engines take advantage of hardware developments. © 2024 IEEE.

关键词： Engines

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