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Fuzzing for Power Grids: A Comparative Study of Existing Frameworks and a New Method for Detecting Silent Crashes in Control Devices

Fuzzing for Power Grids: A Comparative Study of Existing Fra...

引用

2023 IEEE Design Methodologies Conference, DMC 2023

作者： Uwibambe, Marie Louise Pan, Yanjun Li, Qinghua University of Arkansas Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350315547

Power grids serve as the backbone of modern society, supplying electricity for a wide range of critical uses, including the residential, commercial, and industrial sectors. As the power grid infrastructure becomes more reliant on networked digital technologies, it also becomes more vulnerable to cyberattacks. To ensure the resilience and security of the power grid, it is necessary to identify security vulnerabilities in power electronic devices. Fuzzing is an effective technique for detecting security flaws in software/firmware by subjecting the system under test to a series of unexpected inputs, which elicits unexpected behavior such as system crashes. While some work has been done on fuzzing power grid systems, there still lacks a comprehensive, comparative study of existing solutions. Furthermore, new methods are still needed to detect unexpected behaviors of power electronic devices during fuzzing. In this paper, we first conduct a survey and a comparative study of existing fuzzing frameworks for power grid systems. We then hypothesize and verify the use of electromagnetic waves as a method to detect silent system crashes in power electronic devices, aiming to advance fuzzing in this field. © 2023 IEEE.

关键词： Electromagnetic waves

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A New Method Based on Symbolic Regression to Detect The Probability of False Data Injection Attacks on PV Generation 13

A New Method Based on Symbolic Regression to Detect The Prob...

引用

13th Smart Grid Conference, SGC 2023

作者： Moradpour, Amir Mohammad Alizadeh, Mohammad Hossein Delkhosh, Hamed Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350396065

The increasing penetration of renewable energy resources, such as photovoltaic (PV) systems, has caused significant concerns in power systems. As one of theses concerns, the escalating number of reported cyber-attacks worldwide raises major issues about the operation of PV systems, as it may potentially jeopardize the operation of their connected power systems, especially during contingencies. One attack that seriously threatens cyber-physical systems is False Data Injection (FDI). This paper examines and analyzes the detection of data manipulation in a PV power plant using Genetic programming (GP), where a data-driven symbolic regression-based power generation forecasting is utilized and a hybrid probability-based FDI attack detection method is proposed. This method effectively enhances the FDI attack detection speed without sacrificing the accuracy. The presented method has been implemented on the real data set of a PV power plant, and the results show the effectiveness of the proposed model. © 2023 IEEE.

关键词： Genetic algorithms

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Multiple Disease Prediction using Machine Learning and Deep Learning with the Implementation of Web Technology

Multiple Disease Prediction using Machine Learning and Deep ...

引用

2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Rahman, Mostafizur Islam, Saiful Sarowar, Sadia Binta Zaman, Meem Tasfia North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798350322347

Disease prediction is crucial in healthcare, enabling professionals to diagnose and treat diseases more effectively. In recent years, machine learning and web technology have emerged as powerful tools for predicting various diseases. Machine learning algorithms can analyze large and complex datasets to learn patterns and relationships in the data, enabling them to make accurate disease prediction technology. On the other hand, web technology can be used to deploy machine learning models on platforms such as websites or mobile apps, making them accessible to users. This research addresses the need for early detection and diagnosis of diseases like diabetes, heart disease, Parkinson's disease, lung cancer and brain stroke. To achieve this goal, various classification algorithms of machine learning, including Support Vector Machine, k-nearest neighbors, Decision Tree, Random Forest, AdaBoost, Gaussian Naive Bayes etc. and one deep learning model, Long Short-Term Memory, are implemented. The main goal of this paper is to create a web technology for predicting multiple diseases and to outline the steps involved in building such a system. © 2023 IEEE.

关键词： Forecasting

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A Multi-Class Classification Approach for Anemia Level Prediction with Machine Learning Models 8

A Multi-Class Classification Approach for Anemia Level Predi...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Trigka, Maria Dritsas, Elias Mylonas, Phivos University of West Attica Department of Informatics and Computer Engineering Athens Egaleo Greece University of Patras Department of Electrical and Computer Engineering Patras Greece

ISBN: (纸本)9798350383676

It is a common belief that Artificial Intelligence (AI) and Machine Learning (ML) provide researchers and medical experts with concepts, tools, and techniques to build intelligent systems able to analyze, process and detect hidden patterns, in order to turn data into actionable knowledge for personalized medicine and decision-making (e.g., disease prevention via associated risk prediction, treatment, etc.). The present study seeks an ML solution that will facilitate the recognition of anemia level by investigating the performance of two well-established strategies for multi-class classification tasks;the One-Vs-All (OVA) and One-Vs-One (OVO). Under the specific strategies, two well-known ML models were assumed, namely, Logistic Regression (LR) and Support Vector Machines (SVM). The validation of both strategies was carried out in a publicly available dataset by measuring the weighted average performance in all involved classes that capture the anemia levels;accuracy, precision, recall and Area Under the ROC curve (AUC) were captured and compared for the identification of the dominant model and strategy. After the experimental evaluation, the OVO strategy with the LR model is the main suggestion of the current study achieving an accuracy of 95.05 %, precision and recall equal to 0.951 and an AUC of 0.990. © 2023 IEEE.

关键词： Forecasting

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Wireless Acoustic Airborne Jet Propeller 22

Wireless Acoustic Airborne Jet Propeller

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Roy, Akash Barekatain, Matin Lee, Jaehoon Neff, Baptiste Kim, Eun Sok University Of Southern California Department Of Electrical And Computer Engineering United States

ISBN: (纸本)9784886864352

This manuscript presents airborne jet propulsion by audio sounds and ultrasounds through orifices formed by bulk-micromachining of a silicon wafer. The propeller is integrated with a small, printed circuit board (PCB) with a DC/DC converter, an oscillator, and a power amplifier, all powered by a 100F lithium-ion capacitor to make the propeller operable wirelessly. The peak propulsion force of the wireless propeller is measured to be 63.1 mg (or 618 mN) while the packaged wireless propeller's weight is 10.6 g, including the drive electronics and adapter) when driven by 2.5kHz sinusoidal voltage with 21.4Vpp. A wired propeller (with 563 mg weight without adapter) is shown to high jump, long jump, wobbly fly, and propel objects. Also, the propeller is shown to work when driven by ultrasounds with a maximum propulsion force of 8.4 mg (82 mN) when driven by 20kHz, 20Vpp sinusoidal signal. Varying the frequency gradient of the applied sinusoidal pulses is shown to move the propeller to the left or right on demand to reach a specific location. © 2023 IEEJ.

关键词： Airborne Acoustic Propeller Synthetic Air Jet Ultrasonic Propulsion Wireless Acoustic Propeller

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A PDU-based Ethernet Routing using CAN Compression

A PDU-based Ethernet Routing using CAN Compression

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Lee, Jinsun Oh, Sung Bhin Do, YoungSoo Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350326413

In the case of Controller Area Network (CAN), which is widely used in-Vehicle Networks (IVN), there is a problem that low-priority messages are not transmitted as the number of Electronic Control Units (ECU) increases. In this paper, we propose a technique that compresses CAN data using the BFC algorithm and transmits the compressed data as a single message via Ethernet as a gateway. To evaluate the proposed algorithm, experiments were conducted using real vehicle driving data, and the experimental results are presented. © 2023 IEEE.

关键词： Ethernet

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An Ensemble Approach using Self-attention based MobileNetV2 for SAR classification 2

An Ensemble Approach using Self-attention based MobileNetV2 ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Anjali, K.S. Singh, Rimjhim Padam Panda, Manoj Kumar Palaniappan, Kannappan Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical Engineering and Computer Science University of Missouri Columbia United States

The Synthetic Aperture Radar (SAR) image classification has become an essential task in numerous applications. Despite its significance, SAR image classification remains challenging due to the inherent complexity and speckle noise characteristics of SAR data. To address this, an innovative approach is proposed in this study, combining the power of ensemble learning and the efficiency of MobileNetV2 architecture to achieve robust and accurate SAR image classification. The proposed deep learning model consists of two main components: an ensemble learning approach that combines multiple binary classifiers, and the employment of MobilenetV2 architecture with self-attention mechanism as a binary classifier. The ensemble approach uses a max-value selector for predicting final class from the individual classifier output. The MobileNetV2 architecture is finetuned by freezing weights and removing the last four layers. The inclusion of the attention layer within the architecture aids in refining the learning process by focusing on crucial features while disregarding irrelevant background elements in the images. The effectiveness of the model is evaluated using Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark dataset with accuracy of classification as performance *** model is trained and tested with images captured at various aspect angles. This approach enhances the robustness of the results, making them more applicable to real-world scenarios when compared to prior methods. This study also compares the performance of the ensemble method using MobileNetV2 with state-of-art individual classifiers and different CNN architectures. The proposed ensemble method OVA-MbNN achieves an impressive classification accuracy of 76.63%, surpassing the performance of state-of-the-art classifiers while maintaining robustness. © 2024 Elsevier B.V.. All rights reserved.

关键词： Synthetic aperture radar

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An End-To-End Deep Learning Approach for Atherosclerosis Detection using Autoencoders 5

An End-To-End Deep Learning Approach for Atherosclerosis Det...

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5th International Conference on Bio-engineering for Smart Technologies, BioSMART 2023

作者： Baalbaki, Jad M. Kadri, Sara S. El Awad, Karin A. Aridy, Carole H. Al Aridi, Majd S. Zein, Mohamad A. El Diab, Mohammad O. Sabbah, Maher M. Zantout, Rached. N Adla, Yasmine A. Abu Rafik Hariri University Electrical and Computer Engineering Department Mechrif Lebanon

ISBN: (纸本)9798350338492

Cardiovascular disease remains the primary cause of mortality worldwide. Early diagnosis of cardiovascular diseases, particularly atherosclerosis, has been the focus of extensive research. Gated Magnetic Resonance Imaging has enabled researchers to capture high-resolution images of the cardiovascular system, facilitating the detection of arterial wall plaque. The integration of machine learning techniques into the diagnostic process has significantly improved the accuracy of cardiovascular disease diagnosis. This paper proposes a deep learning approach that utilizes autoencoders to identify atherosclerosis in murine subjects from gated MRI images. The images underwent preprocessing using a Block Matching 3D (BM3D) filter to eliminate noise and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance contrast. To evaluate the performance of the proposed model, we trained and tested two different autoencoder architectures, stacked and convolutional. The models successfully distinguished healthy and diseased subjects based on input and reconstructed images. The Convolutional Autoencoder outperformed the Stacked Autoencoder by minimizing the reconstruction loss by 95%, demonstrating superior accuracy in reconstructing medical images. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Design and Analysis of a Hardware Accelerator with FPU-Based Runge-Kutta Solvers 4

Design and Analysis of a Hardware Accelerator with FPU-Based...

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4th International Conference on electrical, Communication and computer engineering, ICECCE 2023

作者： Bhattacharya, Soham Chakraborty, Dwaipayan Department of Electrical and Computer Engineering Rowan University NJ United States

ISBN: (纸本)9798350369694

In several high-speed computer applications, ordinary differential equations (ODEs) are often utilized. Modern CPUs, however, usually offer a modest throughput for these systems. This paper discusses the implemented design and analysis of a hardware accelerator including second-order, third-order, and fourth-order Runge-Kutta ODE solvers for improving the computing speed and accuracy. The implementation of the hardware accelerators has been done using the VHDL programming language and has been deployed on the Zynq ZC702 FPGA evaluation board. A comprehensive comparative analysis of different Runge-Kutta hardware accelerators, highlighting factors including timing performance, hardware resource usage, and the total on-chip power consumption has been given. According to the experimental findings, the fourth-order Runge-Kutta equation has a significant power consumption, rising by an estimated 51.37 percent and 12.445 percent when comparing second-order and third-order Runge-Kutta solvers at a clock rate of 100 MHz, respectively. This study contributes to the enhancement of ODE solutions in high-performance computing applications. © 2023 IEEE.

关键词： Ordinary differential equations

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Methods of data streaming from IEC 61499 applications to Cloud storages 32

Methods of data streaming from IEC 61499 applications to Clo...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Lyu, Tuojian Galkin, Nikolai Liakh, Tatiana Yang, Chen-Wei Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Computer and Space Engineering Department of Computer Science Sweden

ISBN: (纸本)9798350399714

This paper presents and discusses two methods for collecting data from decentralised control applications designed in IEC 61499 architecture. The topic is justified by the growing use of Cloud-based storage and presentation services in the Internet-of-Things systems. Enabling IEC 61499 devices with the capabilities of storing data in the Cloud and using web-based data presentation and data analytics is of great practical importance. Moreover, what is the most elegant way to configure such data streaming in IEC 61499 is an open question, to solving which this paper aims to provide state-of-the-art information. © 2023 IEEE.

关键词： Data Analytics

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