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Adversarial Machine Learning Attack on Machine Learning-based Controller for Solar Inverters

Adversarial Machine Learning Attack on Machine Learning-base...

引用

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Joaquin Massa Kyoung-Tak Kim Taesic Kim BoHyun Ahn Kumar Venayagamoorthy Jianwu Zeng Dept. of Electrical Engineering and Computer Science Texas A&M University-Kingsville Kingsville USA Dept. of Electrical Engineering Soongsil University Seoul Republic of Korea Dept. of Electrical Engineering and Computer Science University of Missour-Columbia Columbia USA Dept. of Electrical and Computer Engineering Clemson University Clemson USA Dept. of Electrical and Computer Engineering and Technology Minnesota State University Mankato Mankato USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Adversarial machine learning (ML) attacks are stealthy attacks designed to mislead the ML model results. This paper explores adversarial ML attacks that generate adversarial noisy input data in an ML-based controller in a solar inverter. Three types of ML models, long short-term memory (LSTM), gated recurrent unit (GRU)), and bidirectional-LSTM (Bi-LSTM), are designed to replace proportional-integral (PI) controller-based vector control for a solar inverter and two white-box adversarial ML attacks (Basic Iterative Method (BIM) attack and Fast Sign-Gradient Method (FGSM)) are applied to the ML controllers. It is observed that the adversary ML attacks designed in stealthy way do not affect the PI controller, while significantly degrading performance of the ML-based controllers. Moreover, the BIM attack is more effective than FGSM and Bi-LSTM-based controller is relatively robust to the attacks compared to peer.

关键词： PI control Inverters Adversarial machine learning Hardware Robustness Real-time systems Noise measurement Long short term memory Machine vector control Microprogramming

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RL-Driven Fine-Grained Power Gating for Modern Processors Using Multi-Layer Perceptron Models

RL-Driven Fine-Grained Power Gating for Modern Processors Us...

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IEEE International Symposium on Quality Electronic Design

作者： Naman Kalra Jaynarayan T Tudu Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Dept. of Computer Science Engineering Indian Institute of Technology Tirupati India

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

This paper introduces an RL-driven algorithm for fine-grained power gating of functional units in superscalar processors. The proposed algorithm leverages a Multi-Layer Perceptron (MLP) neural network as a policy model, trained by a reinforcement learning (RL) agent, to optimize power states across four modes: On, Sleep-on, Sleep-off, and Off. By statically analyzing features extracted from the control flow graph (CFG) and execution profile of the program, the RL-driven model selects the most energy-efficient power states for each functional unit without compromising performance. Traditional power gating techniques often struggle with tradeoffs between minimizing performance loss (wake-up latency) and reducing power consumption, necessitating a reevaluation of power management strategies. Next-generation complex SoC designs at < 7nm technology nodes require a finer granularity approach to effectively manage power states. Our AI-based algorithm provides an optimized trade-off between power efficiency and performance by enabling power gating across multiple power states and selecting the power state that has the effectively least energy-delay product. Additionally, we propose an architectural circuit design for power gating multiple FU's within a superscalar processor, enabling efficient state transitions. Preliminary experimental results show that the proposed AI-based method achieves 10-20% energy savings while improving performance by 1.5% compared to traditional power gating algorithms.

关键词： Program processors Power demand Power system management Reinforcement learning Performance gain Feature extraction Energy efficiency Circuit synthesis Flow graphs Next generation networking

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Long Short-Term Memory-Based Next Keyword Prediction 6

Long Short-Term Memory-Based Next Keyword Prediction

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Rajkumar, K. Karthikeyan, S. Hariharan, U. College of Engineering and Technology SRM Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur India Department of CSE Bangalore562112 India Apex Institute of Technology Chandigarh University Department of CSE Punjab India

ISBN: (纸本)9798350306613

This paper focuses on a method that uses an LSTM model to predict the next word of a sentence. Based on Katz's Backoff model, it has been designed to improve and further succeed in our previous work. The main intention here is to show how effective the LSTM model is when compared to Katz's backoff model in terms of the number of n-grams required to predict the next word, and it also helps you predict more accurately. Here LSTM is used for processing, prediction and classification. Since the LSTM model is efficient and has been modified to the task at hand, the model offers very highly accurate predictions even though have used only a tiny portion of the entire corpus. When comparing the existing model, the proposed model predicts the word with accuracy of 90%. © 2024 IEEE.

关键词： Katz's backoff Model LSTM Prediction

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Novel Plant Leaf Disease Detection Approach using Hybrid Deep Learning Strategy 5

Novel Plant Leaf Disease Detection Approach using Hybrid Dee...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Vamshi, Jalla Tanwar, Shashi Thatipudi, Jayendra Gopal Ponsangeetha, A. Mukherjee, Saptarshi Mandhare, Jagdish B. School of Agriculture Sr University Telangana Warangal India G D Goenka University Department- Computer Science and Engineering Haryana Gurugram India University of North Texas Dept. of Computer Science United States Velammal Institute of Technology Department of Computer Science and Engineering Panchetti India Goldsmith Institute of Advanced Research Department-Mtech Bioinformatics Giar West Bengal Kolkata India Bharati Vidyapeeth College of Engineering Department of Instrumentation Navi Mumbai India

ISBN: (纸本)9798350385649

The early detection and identification of plant diseases are pivotal for precision agriculture, aiming to mitigate crop losses and optimize yields. This study presents a novel approach to plant disease identification, leveraging Deep Learning (DL) techniques with a focus on transfer learning. Traditional methods often rely on complex image enhancement, disease region segmentation, and feature extraction, whereas our approach employs Convolutional Neural Networks (CNNs) for efficient and accurate disease detection. To enhance accessibility and diagnostic capabilities, we implement Hybrid DL Strategy, which consists of EfficientNetB0 and MobileNetV2 model, tailored for lightweight applications suitable for smartphone implementation. Efficient NetB0 model, which considers dept., width, and resolution during convolution, enhancing the model's capacity to capture intricate features critical for accurate disease diagnosis. The overall accuracy of proposed method is 98.44%, surpassing CNN, ResNet, and AlexNet. This high accuracy underscores the effectiveness of the proposed model in correctly classifying both diseased and healthy plant leaves. © 2024 IEEE.

关键词： Convolutional neural networks

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Pruning Binarized Neural Networks Enables Low-Latency, Low-Power FPGA-Based Handwritten Digit Classification

Pruning Binarized Neural Networks Enables Low-Latency, Low-P...

引用

IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Syamantak Payra Gabriel Loke Yoel Fink Joseph D. Steinmeyer Dept. of Electrical Engineering Stanford University (formerly Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology) Dept. of Materials Science and Engineering Massachusetts Institute of Technology Dept. of Materials Science and Engineering Dept. of Electrical Engineering and Computer Science and Institute for Soldier Nanotechnologies Massachusetts Institute of Technology Dept. of Electrical Enigneering and Computer Science Massachusetts Institute of Technology

As neural networks are increasingly deployed on mobile and distributed computing platforms, there is a need to lower latency and increase computational speed while decreasing power and memory usage. Rather than using FPGAs as accelerators in tandem with CPUs or GPUs, we directly encode individual neural network layers as combinational logic within FPGA hardware. Utilizing binarized neural networks minimizes the arithmetic computation required, shrinking latency to only the signal propagation delay. We evaluate size-optimization strategies and demonstrate network compression via weight quantization and weight-model unification, achieving 96% of the accuracy of baseline MNIST digit classification models while using only 3% of the memory. We further achieve 86% decrease in model footprint, 8mW dynamic power consumption, and $< 9\text{ns}$ latency, validating the versatility and capability of feature-strength-based pruning approaches for binarized neural networks to flexibly meet performance requirements amid application resource constraints.

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Advanced Forecasting and Analysis Techniques for Optimizing Website Performance: A Comparative Study of Time Series Models

Advanced Forecasting and Analysis Techniques for Optimizing ...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Gayathri Raikar Harshith D N. Guruprasad Dept. of Computer Science and Engineering Global Academy of Technology Bangalore India Computer Science and Engineering Buddha Institute of Technology Gorakhpur India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Analyzing website performance is crucial for optimizing a site's functionality, enhancing user engagement, and aligning with business goals. This evaluation focuses on essential metrics such as average engagement time per session, engagement rate, and events per session, which significantly impact user experience and overall business success. Through effective performance analysis, organizations can improve user satisfaction, boost conversion rates, and enhance their profitability and reputation. This paper reviews different approaches for analyzing these key metrics, explores their effects on user behavior and business performance, and offers best practices for optimizing website performance. By employing detailed performance analysis, businesses can make informed decisions and realize their strategic goals in the digital environment.

关键词： Measurement Reviews Profitability Time series analysis Organizations Predictive models User experience Data models Performance analysis Forecasting

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Detecting Ransomware Threats Using Machine Learning Approach

Detecting Ransomware Threats Using Machine Learning Approach

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Sunitha Guruprasad Arnav G Rao Sanmitha Chitharanjan Shetty Rose Bastian Sarah Abdul Khader Dept. of Computer Science & Engineering St Joseph Engineering College Mangaluru India Visvesvaraya Technological University Belagavi Karnataka India Dept. of Computer Science & Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

The rise of ransomware-related cyber-attacks poses a severe threat to organizations across various sectors. This research addresses the urgent need for robust detection mechanisms using advanced machine learning (ML) models. Ransomware, characterized by irreversible encryption of user data, demands proactive strategies for identification and prevention. This study leverages publicly available ransomware and benign executable files from sources such as VirusTotal which are run in a sandboxed environment to obtain a dataset to train an ML model capable of differentiating between diverse ransomware samples and classifying them based on specific characteristics. The process begins with meticulous data preprocessing ensuring the dataset's quality. Feature extraction algorithms are employed to identify relevant characteristics, streamlining the dataset and optimizing model performance. The model is trained on the dataset to effectively learn patterns. Evaluation metrics, including accuracy, precision, recall, F1-score, and the ROC curve, provide comprehensive insights into the model's effectiveness.

关键词： Analytical models Accuracy Computational modeling Stacking Telecommunication traffic Very large scale integration Feature extraction Ransomware Integrated circuit modeling Random forests

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Multimodal Representation Learning:Cross-modality and Shared Representation

Multimodal Representation Learning:Cross-modality and Shared...

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2022 International Conference on Industry 4.0 Technology, I4Tech 2022

作者： Mohan, Paul Brilley, Batley C Kumar A. A, Nippun Amrita School of Engineering Dept. of Computer Science & Engineering Bengaluru India Ai and Automation Litmus 7 Kochi India

ISBN: (数字)9781665471961

ISBN: (纸本)9781665471961

Various kinds of Information retrieval or processing task can be difficult basis on how the information is viewed or represented. Representation learning is a technique that allows a system to discover the representation of the data which used for detecting features and classifying things. As compared with learning from one representation, learning from different representations make the task easier. For example, learning something from images and their texts or learning from video and their audio produces good results than learning from one source (either image or text)or (video or audio). Learning from multiple modalities helps to achieve the target correctly within short time. This paper tries to design a Multimodal Autoencoder with minimum loss that can produce shared representation of images and its texts © 2022 IEEE.

关键词： Learning systems

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A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

A Deep Learning Based Image Processing Technique for Early L...

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Emerging Technologies for Sustainability and Intelligent systems (ICETSIS), ASU International Conference in

作者： Nowshin Tasnim Kazi Rifah Noor Mursalina Islam Mohammad Nurul Huda Iqbal H Sarker Dept. of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of Computer Science & Engineering United International University Dhaka Bangladesh Edith Cowan University Perth WA Australia

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology.

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Early Prediction of Cardiovascular Diseases Using Feature Selection and Machine Learning Techniques 6

Early Prediction of Cardiovascular Diseases Using Feature Se...

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6th IEEE International Conference on Communication and Electronics systems, ICCES 2021

作者： Rashme, Tamanna Yesmin Islam, Linta Jahan, Sohely Prova, Ayesha Aziz Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Barishal Dept. of Computer Science and Engineering Dhaka Bangladesh Central Women's University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665435871

cardiovascular disease is one of the most important diseases that affects the heart and blood vessels. The loss of lives is mostly linked to a lack of early disease detection, and a preemptive prediction of cardiovascular disease risk will greatly alleviate the situation. Due to the increasing amount of data growth in the health care industry, therefor Machine Learning techniques predict the disease depends on the severity of the patient's side effect. This research work proposes a model to perform early prediction of cardiovascular disease by using different machine learning algorithms, which are used for different prediction purposes. For feature selection purposes, Random Forest algorithm is used to select suitable attributes for the prediction process. The proposed model is assessed based on evaluation metrics;accuracy, precision, recall (sensitivity), f1- score, and specificity. In this exploration of predicting cardiovascular disease, the XGBoost machine learning classifier accomplished a higher rate of accuracy 75.10%. Also, this model provides a higher rate for other evaluation metrics for all the evaluation metrics 76.64%, 69.88%, 79.32%, 78.16%, and 72.84% for precision, sensitivity, specificity, and f1-score, respectively in the case of early cardiovascular diseases prediction. © 2021 IEEE.

关键词： Forecasting

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