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Cyber Attacks Detection using Deep Learning Methods for Resilient Operation in DC Shipboard Microgrids

Cyber Attacks Detection using Deep Learning Methods for Resi...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Ali, Zulfiqar Hussain, Tahir Su, Chun-Lien Jurcut, Anca Delia Baloch, Shazia Sadiq, Muhammad National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung city Taiwan Graduate School of Informatics and Engineering The University of Electro-Communication Department of Informatics Tokyo Japan School of Computer Science University College Dublin BelfieldDublin 4 Ireland Balochistan University of Engineering and Technology Department of Electrical Engineering Khuzdar Pakistan

ISBN: (纸本)9798350352290

Cyber resilience has become paramount as a transition of maritime systems towards digitization, particularly within DC shipboard microgrids (SMGs). Adopting innovative communication technologies can enhance the resilience of SMGs for stable operation. However, challenges like false data injection and Man-in-The-Middle attacks pose significant threats to SMG operations when integrating these technologies into ship intelligent grids. In this regard, this paper proposes a reliable deep learning (DL) method, especially an Artificial neural network (ANN) with a deep encoder-decoder architecture, for the detection of cyber-intrusions and mitigating their effects, ensuring system control and stability for resilient operation of SMG. Detecting malicious data intrusions is crucial for maintaining optimal grid conditions and preventing disruptions in load dispatch. The proposed method utilizes a fusion of current and voltage data features for comprehensive DL model training, resulting in an adequate level of detection accuracy and providing cybersecurity analysis for SMGs, addressing component-level attacks, and devising defense strategies from aspects of detection, mitigation, and prevention. Furthermore, the deep ANN is fine-tuned with optimal hyperparameters to effectively counter cyberattacks, achieving an enhanced accuracy rate of 97.51% and minimal loss of 0.101%, surpassing conventional machine learning approaches. Rigorous test scenarios are performed to validate the robustness of the proposed method, emphasizing the cyber resilience of DC SMGs for enhanced security and operational integrity. © 2024 IEEE.

关键词： Network intrusion

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Self-supervised Pretraining for 2D Medical Image Segmentation 17th

Self-supervised Pretraining for 2D Medical Image Segmentati...

引用

17th European Conference on Computer Vision, ECCV 2022

作者： Kalapos, András Gyires-Tóth, Bálint Department of Telecommunications and Media Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. Budapest1111 Hungary

ISBN: (纸本)9783031250811

Supervised machine learning provides state-of-the-art solutions to a wide range of computer vision problems. However, the need for copious labelled training data limits the capabilities of these algorithms in scenarios where such input is scarce or expensive. Self-supervised learning offers a way to lower the need for manually annotated data by pretraining models for a specific domain on unlabelled data. In this approach, labelled data are solely required to fine-tune models for downstream tasks. Medical image segmentation is a field where labelling data requires expert knowledge and collecting large labelled datasets is challenging;therefore, self-supervised learning algorithms promise substantial improvements in this field. Despite this, self-supervised learning algorithms are used rarely to pretrain medical image segmentation networks. In this paper, we elaborate and analyse the effectiveness of supervised and self-supervised pretraining approaches on downstream medical image segmentation, focusing on convergence and data efficiency. We find that self-supervised pretraining on natural images and target-domain-specific images leads to the fastest and most stable downstream convergence. In our experiments on the ACDC cardiac segmentation dataset, this pretraining approach achieves 4–5 times faster fine-tuning convergence compared to an ImageNet pretrained model. We also show that this approach requires less than five epochs of pretraining on domain-specific data to achieve such improvement in the downstream convergence time. Finally, we find that, in low-data scenarios, supervised ImageNet pretraining achieves the best accuracy, requiring less than 100 annotated samples to realise close to minimal error. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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Implementation of Multi-Hop Mesh Networking Using ESP32 for IoT Communication 4

Implementation of Multi-Hop Mesh Networking Using ESP32 for ...

引用

4th International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Ramadhan, Saufik Syafalni, Infall Sutisna, Nana Adiono, Trio School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia University Center of Excellence on Microelectronics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331506490

Today, the demand for reliable and efficient communication systems is more critical than ever, especially with the rapid growth of IoT applications. In this paper, we propose the implementation of a multi-hop network using ESP32 modules, aiming to address the challenges of long-range communication while maintaining stable data transmission. The network design consists of several ESP32 devices placed at various distances to simulate a real-world scenario where connectivity can be challenged by distance and environmental factors. We also incorporated antennas into the setup to enhance the transmission power and ensure reliable communication across multiple hops. Our experimental results show that multi-hop networks, which consist of four hops, achieve a maximum data rate of 13 Mbps and a minimum data rate of 5.9 Mbps. These results demonstrate the network's ability to maintain consistent performance even as the data travels through multiple relays. Additionally, the network successfully covered a distance of up to 198 meters, proving the feasibility of using ESP32-based multi-hop networks in large-scale IoT deployments. The findings of this study are particularly valuable for IoT applications in areas such as agriculture, manufacturing, and smart cities. In these sectors, where devices often need to communicate over long distances or in environments with obstacles, the multi-hop network can provide a reliable communication solution. By extending the range of the ESP32, this work opens new possibilities for building scalable, efficient, and robust IoT networks capable of handling large volumes of data in diverse environments. © 2024 IEEE.

关键词： MESH networking

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Live traffic analysis on S1-MME interface using LSTM autoencoder 3

Live traffic analysis on S1-MME interface using LSTM autoenc...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Ficzere, Daniel Hollosi, Gergely Varga, Pal Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Dept. of Telecommunications and Media Informatics Muegyetem rkp. 3 BudapestH-1111 Hungary

ISBN: (纸本)9798350333732

The spreading of mobile communication networks resulted in a vast amount of signaling traffic on the control plane. It is quite challenging to analyze the extensive number of message sequences, recognize patterns and identify anomalies manually. However, cutting-edge machine learning methods offer excellent tools to explore unusual events in signaling traffic to provide countermeasures in the mobile network. The paper proposes a data exploration method with an LSTM-Autoencoder based latent space model to visualize manifolds controlling the generation of control plane traffic. The model has been validated on a live LTE signaling traffic captured on the S1-MME interface. © 2023 IEEE.

关键词： Learning systems

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Pet Watching System with IoT Devices and Chatbots

Pet Watching System with IoT Devices and Chatbots

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Nakashige, Mutsuhiro Shibusawa, Ryota Oe, Katsutoshi Shonan Institute of Technology Faculty of Informatics Kanagawa Japan Daiichi Institution of Technology Faculty of Engineering Kagoshima Japan Nippon Bunri University Faculty of Engineering Oita Japan

ISBN: (纸本)9798350324136

The demand for pets has increased as people spend more time at home due to the Corona disaster, and many animals are sold and kept. However, people are gradually returning to normal work and school routines, and the amount of time they spend with their pets is decreasing. Therefore, to maintain a psychological distance from pets, we devised a system that allows users to monitor and interact with the status of pets left at home even when they are away from home. Specifically, the system aggregates and manages the values of various sensors attached to the pet's rearing cage in the cloud, and a chatbot communicates with the user by interpreting the data. This prototype system was linked to a smartphone SNS application and operated for about 2 years and a half. In this paper, we report on the verification of the system's operation and the effects observed. © 2024 IEEE.

关键词： Microcontroller board with Wi-Fi Remote pet watching SNS Chatbots

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Machine Learning Vulnerability Assessment of Ring Oscillator Physical Unclonable Functions

Machine Learning Vulnerability Assessment of Ring Oscillator...

引用

2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Kareem, Husam Dunaev, Dmitriy Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Automation and Applied Informatics Muegyetem rkp. 3. BudapestH-1111 Hungary

ISBN: (纸本)9798350347074

Devices authentication and cryptographic keys production techniques are implemented in various security applications. Because of its ability to generate unique, unclonable, and unpredictable cryptographic keys, physically unclonable functions (PUFs) have been utilized in various hardware security applications. However, PUFs can be vulnerable to adversary attacks like machine learning (ML)-based modeling attacks. Input-output combinations of PUFs are called challenge-response pairs (CRPs). This article provides a vulnerability assessment of three recent Ring Oscillator (RO) PUFs approaches against ML modeling attacks. Assuming that adversary obtains part of the RO-PUF CRPs, and they can be used to predict the unrevealed CRPs. The article has a secondary objective to assess the impact of aging on the tested RO-PUF approaches in regenerating the same response. © 2023 IEEE.

关键词： Machine learning

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Diffusion probabilistic model based face anonymization in embedded environments 14

Diffusion probabilistic model based face anonymization in em...

引用

14th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2023

作者： Morvay, Balázs Tibor Béres, Bálint Torma, Szabolcs Szegletes, Luca Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economic Műegyetem rkp. 3 BudapestH-1111 Hungary

ISBN: (纸本)9798350325652

Computer vision systems depend on vast and diverse datasets to achieve optimal performance. However, in scenarios where data collection involves personal identifiable information, privacy-protecting legislation can impede the acquisition process. To address this challenge, we present a novel face anonymization pipeline capable of running on embedded platforms. The proposed methodology combines denoising diffusion probabilistic models (DDPMs) and advanced image super-resolution techniques, such as DFDNet and ESPCN, to achieve high-quality anonymized facial images while preserving crucial facial keypoint locations. We conduct a comprehensive evaluation of the pipeline’s performance, encompassing image quality, keypoint precision, and efficiency on two popular single-board computers, namely the Nvidia Jetson Nano and the Raspberry Pi 4B. The source code is available at https://***/balazsmorv/facediffusion. © 2023 IEEE.

关键词： Pipelines

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Numerical Study on W-type Collecting Plates with Multiple Corona Electrodes of Electrostatic Precipitators 1

引用

16th International Conference on Electrostatic Precipitation, ICESP 2022

作者： Bani Fayyad, Mo’ath Asipuela González, Angel Iváncsy, Tamás Department of Electric Power Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9783031345265

ISBN: (纸本)9783031345258

The optimal arrangement of collecting plates and corona wires considerably impacts the collection efficiency of Electrostatic Precipitators (ESPs). Many experts are striving to improve existing models in a variety of methods. This research aims to examine the effects of using multiple corona wires with W-type collecting plates on the properties of electrostatic precipitators. Three different types of corona wire arrangements with W-type collecting plates were modeled and compared with Flat Plates (FPs) to evaluate the effect of using multiple corona electrodes on the electrical field distribution, space charge density distribution, current density distribution, and particle collection efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electrostatic precipitators

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Competency Framework in Higher Education: A Bibliometric Analysis from 2000 to 2023 24

Competency Framework in Higher Education: A Bibliometric Ana...

引用

8th International Conference on Education and Multimedia technology, ICEMT 2024

作者： Tjandra, Ellysa Ferdiana, Ridi Setiawan, Noor Akhmad Kusumawardani, Sri Suning Department of Informatics Faculty of Engineering University of Surabaya Surabaya Indonesia Department of Electrical Engineering and Information Technology Gadjah Mada University Yogyakarta Indonesia

ISBN: (纸本)9798400717611

This study performs a bibliometric mapping analysis of the competency framework in higher education research utilizing the Scopus database using the Search, Appraisal, Synthesis, and Analysis (SALSA) technique. Our study comprehensively outlines the competency framework research from 2000 to 2023. We analyze and synthesize previous works to uncover topic areas, highlight prospective research subjects, and identify common trends and patterns. We collected all journal and conference publications from 2000 to 2023, from which 366 papers were examined further using the VOSviewer software to determine the co-authorship coupling between documents and discover the most frequently used keywords. This study finds that the competency framework is still being researched worldwide and shows an increasing trend, significantly from 2019 and culminating in 2021 and 2023. Competency framework research is primarily explored in Social Science, Medicine, Nursing, Computer Science, Business, Management, and Accounting. It is performed in 63 countries, mainly in developed countries: the United States, the United Kingdom, and Canada. This paper also finds the main contributors to competency framework studies. We discovered that studies on competency framework are grouped into six clusters of keyword co-occurrences, and the highest linked keywords are"human,""humans,""article,""curriculum,""education,""medical education," and"clinical competence." We also provide further analysis and find the newest related keywords that can be used as a basis for future studies. © 2024 Copyright held by the owner/author(s).

关键词： Medical education

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PIC-XAI: Post-hoc Image Captioning Explanation using Segmentation 17

PIC-XAI: Post-hoc Image Captioning Explanation using Segment...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Al-Shouha, Modafar Szucs, Gabor Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics Műegyetem rkp. 3 BudapestH-1111 Hungary

ISBN: (纸本)9798350321104

The rapid advancement in Deep Learning (DL) proposes viable solutions to various real-world problems. However, deploying DL-based models in some applications is hindered by their black-box nature and the inability to explain them. This has pushed Explainable Artificial Intelligence (XAI) research toward DL-based models, aiming to increase the trust by reducing their opacity. Although many XAI algorithms were proposed earlier, they lack the ability to explain certain tasks, i.e. image captioning (IC). This is caused by the IC task nature, e.g. the presence of multiple objects from the same category in the captioned image. In this paper we propose and investigate an XAI approach for this particular task. Additionally, we provide a method to evaluate XAI algorithms performance in the domain1.1Our code is available at https://***/modafarshouha/PIC-XAI. © 2023 IEEE.

关键词： Image segmentation

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