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Anomaly-based Intrusion Detection using GAN for Industrial control systems 10

Anomaly-based Intrusion Detection using GAN for Industrial C...

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10th International conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Kk, Sabari Shrivastava, Saurabh Sangeetha, V. TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

In recent years, cyber-attacks on modern industrial control systems (ICS) have become more common and it acts as a victim to various kind of attackers. the percentage of attacked ICS computers in the world in 2021 is 39.6%. To identify the anomaly in a large database system is a challenging task. Deep-learning model provides better solutions for handling the huge dataset with good accuracy. On the other hand, real time datasets are highly imbalanced with their sample proportions. In this research, GAN based model, a supervised learning method which generates new fake samples that is similar to real samples has been proposed. GAN based adversarial training would address the class imbalance problem in real time datasets. Adversarial samples are combined with legitimate samples and shuffled via proper proportion and given as input to the classifiers. the generated data samples along with the original ones are classified using various machine learning classifiers and their performances have been evaluated. Gradient boosting was found to classify with 98% accuracy when compared to other © 2022 ieee.

关键词： Generative adversarial networks

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data-driven MODEL FOR ESTIMATION OF SoH AND INTERNAL RESISTANCE OF LIthIUM ION BATTERY BASED ON DEEP NEURAL NETWORK 10

DATA-DRIVEN MODEL FOR ESTIMATION OF SoH AND INTERNAL RESISTA...

引用

10th Indian control conference, ICC 2024

作者： Mehanthika, S.V. Rishi, D. Sutha, S. Pappa, N. Dept of Instrumentation Engg MIT Campus Anna University Chennai India

ISBN: (纸本)9798331517212

Accurate estimation of State of Health (SoH) and Internal Resistance (IR) is essential for optimized performance and longevity, particularly in the new-era of electric vehicles and renewable energy storage devices. the traditional methods struggle with battery data complexity in dynamic situations to enhance the above parameters. this paper presents a data-driven model with hybrid architecture that combines CNN and LSTM models which revolutionize the battery management and focusing on the SoH and IR estimation in dynamic environments, CNN-LSTM models excel by extracting spatial features and learning temporal dependencies within the battery datasets. this increases accuracy and enables real-time monitoring, diagnosis, and optimization of battery health. this paper also shows the execution of LSTM with and without sampling of the datasets for efficient runtime and produces accurate results, this marks a significant leap in the battery management and enabling smarter resource utilization for sustainable energy solutions. © 2024 ieee.

关键词： Deep neural networks

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CNN-based Deep Transfer learning Approach for Detecting Breast Cancer in Mammogram Images 10

CNN-based Deep Transfer Learning Approach for Detecting Brea...

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10th ieee conference on systems, Process and control, ICSPC 2022

作者： Zebari, Dilovan Asaad Haron, Habibollah Sulaiman, Dawlat Mustafa Yusoff, Yusliza Mohd Othman, Mohamad Nurfalihin Nawroz University College of Science Department of Computer Science Kurdistan Region Duhok42001 Iraq Universiti Teknologi Malaysia Faculty of Computing Johor Johor Bahru81310 Malaysia Duhok Polytechnic University Technical College of Administration Department of Information Technology Management Iraq

ISBN: (纸本)9781665470988

Researchers are motivated to investigate deep learning (DL) techniques for mammogram images because of the limitations of conventional systems-based computer-aided detection (CAD) for mammography, the extreme significance of early breast cancer identification, and the high impact of false diagnosis on patients. this study develops a model for breast cancer detection from mammogram images that employ Convolutional Neural Network (CNN) based Transfer learning (TL). the developed structure is comprised of several stages: breast region extraction is performed to extract the Region of Interest (ROI) from the background and artifacts, a Gaussian filter is employed for noise reduction, and data augmentation is carried out to increase the size of the original images for better learning of CNN. then, a pre-trained model-based CNN using an augmented dataset is presented. Deep features are extracted from CNN and they are trained based on using TL. A mini-MIAS dataset was conducted as the testing ground for this experiment, and the best accuracy achieved was 95.71%. the developed framework performs significantly better than other current methods when compared to them. © 2022 ieee.

关键词： Mammography

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A Spatial-temporal Graph based Hybrid Infectious Disease Model with Application to COVID-19 10th

A Spatial-temporal Graph based Hybrid Infectious Disease Mod...

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10th International conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Zheng, Yunling Li, Zhijian Xin, Jack Zhou, Guofa UC Irvine Dept Math Irvine CA 92697 USA UC Irvine Dept Hlth Sci Irvine CA USA

ISBN: (纸本)9789897584862

As the COVID-19 pandemic evolves, reliable prediction plays an important role in policymaking. the classical infectious disease model SEIR (susceptible-exposed-infectious-recovered) is a compact yet simplistic temporal model. the data-driven machine learning models such as RNN (recurrent neural networks) can suffer in case of limited time series data such as COVID-19. In this paper, we combine SEIR and RNN on a graph structure to develop a hybrid spatio-temporal model to achieve both accuracy and efficiency in training and forecasting. We introduce two features on the graph structure: node feature (local temporal infection trend) and edge feature (geographic neighbor effect). For node feature, we derive a discrete recursion (called I-equation) from SEIR so that gradient descend method applies readily to its optimization. For edge feature, we design an RNN model to capture the neighboring effect and regularize the landscape of loss function so that local minima are effective and robust for prediction. the resulting hybrid model (called IeRNN) improves the prediction accuracy on state-level COVID-19 new case data from the US, out-performing standard temporal models (RNN, SEIR, and ARIMA) in 1-day and 7-day ahead forecasting. Our model accommodates various degrees of reopening and provides potential outcomes for policymakers.

关键词： COVID-19 Machine learning Spatial-Temporal Graph RNN

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10th International conference on Model and data Engineering, MEDI 2021

10th International Conference on Model and Data Engineering,...

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10th International conference on Model and data Engineering, MEDI 2021

ISBN: (纸本)9783030784270

the proceedings contain 24 papers. the special focus in this conference is on Model and data Engineering. the topics include: Social Neural Hybrid Recommendation with Deep Representation learning;cloud Query Processing with Reinforcement learning-Based Multi-objective Re-optimization;top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters;anonymization Methods of Structured Health Care data: A Literature Review;categorical Modeling of Multi-model data: One Model to Rule them All;guaranteeing Information Integrity through Blockchains for Smart Cities;a Blockchain-Based Platform for the e-Procurement Management in the Public Sector;COVIDonto: An Ontology Model for Acquisition and Sharing of COVID-19 data;k-Nearest Neighbors Classification of Semantic Web Ontologies;model-Based Approach for Co-optimization of Safety and Security Objectives in Design of Critical Architectures;automated Generation of datasets from Fishbone Diagrams;GPU-Based Algorithms for Processing the k Nearest-Neighbor Query on Disk-Resident data;revisiting data Compression in Column-Stores;using Multidimensional Skylines for Regret Minimization;enhancing Sedona (formerly GeoSpark) with Efficient k Nearest Neighbor Join Processing;towards a Model-Based Approach to Support Physical Test Process of Aircraft Hydraulic systems;multi-facets Contract for Modeling and Verifying Heterogeneous systems;an Approach to Detect Cyberbullying on Social Media;an Ontology Engineering Case Study for Advanced Digital Forensic Analysis;more Automation in Model driven Development;realisability of control-State Choreographies;A Low-Cost Authentication Protocol Using Arbiter-PUF.

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10th International conference on Smart Cities and Green ICT systems, SMARTGREENS 2021, and 7th International conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2021

10th International Conference on Smart Cities and Green ICT ...

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10th International conference on Smart Cities and Green ICT systems, SMARTGREENS 2021, and 7th International conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2021

ISBN: (纸本)9783031170973

the proceedings contain 22 papers. the special focus in this conference is on Smart Cities and Green ICT systems. the topics include: A New Traffic Sign Detection Technique Using Two-Stage Convolutional Neural Networks;Traffic Estimation and MPC-Based Traffic Light System control in Realistic Real-Time Traffic Environments;Solving Complex Intersection Management Problems Using Bi-level MINLPs and Piecewise Linearization Techniques;interaction-Aware Motion Prediction at Highways: A Comparison of three Lane Changing Models;a Hierarchical Dependency-driven Scenario-Based Testing for Autonomous Vehicles;optimal control of Traffic Flow Based on Reinforcement learning;detecting Extended Incidents in Urban Road Networks for Organic Traffic control Using Density-Based Clustering of Traffic Flows;cooperative Driving: Research on Generic Decentralized Maneuver Coordination for Connected and Automated Vehicles;free-Flow Speed Profile Prediction for Tangent Segments on Urban Roads;identifying Requirements to Model a data Lifecycle in Smart City Frameworks;interpretable Privacy-Preserving Collaborative Deep learning for Taxi Trip Duration Forecasting;development of a Safe Charging Infrastructure System: Requirements, Design, Verification and Validation;digital Energy: Towards Comprehensive Digital Support for a Renewable-Based Energy Sector;A Spatial-Temporal Comparison of EV Charging Station Clusters Leveraging Multiple Validity Indices;Facade Material Identification for Optimizing Building Energy Consumption Across Various WWRs for ECBC(Code) Compliant Buildings in Warm-Humid Climate, India;hierarchical Clustering of Complex Energy systems Using Pretopology;analysis of Citizen’s Feedback from the Lens of Smart City Framework: A Case Study Based Approach;a Decision Support System Based on Rainfall Nowcasting and Artificial Neural Networks to Mitigate Wastewater Treatment Plant Downstream Floods;building Rich Interior Hazard Maps for Public Safety.

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Policy learning with Deep Koopman Operator Networks 8

Policy Learning with Deep Koopman Operator Networks

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8th ieee International conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhao, Dongdong Li, Yichang Yang, Xiaodi Yan, Shi School of Information Science and Engineering Lanzhou University Gansu730000 China

ISBN: (纸本)9798350300178

In this paper, a deep policy learning method based on Koopman operator networks is proposed for reinforcement learning tasks. Specifically, a deep neural network based on the Koopman operator is designed for nonlinear systems in reinforcement learning, and the environment dynamics are embedded in a high-dimensional space to obtain a highly approximate linear model. An iterative linear quadratic regulator method based on a value function is also proposed to complete the learning and solution of the optimal policy. the proposed model-based policy learning algorithm makes the policy in the reinforcement learning task easy to solve and improves the data efficiency and algorithm performance. the results of robot control experiments conducted at OpenAI Gym show that the proposed control algorithm has better control performance than the representative reinforcement learning and data-driven methods. © 2023 ieee.

关键词： Reinforcement learning

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Proceedings of 2020 ieee PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020

Proceedings of 2020 IEEE PES Innovative Smart Grid Technolog...

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10th ieee PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020

ISBN: (纸本)9781728171005

the proceedings contain 240 papers. the topics discussed include: analysis of the electrical properties of SimBench low voltage benchmark network models;aggregator’s business models: challenges faced by different roles;analysis of deep-heat energy wells for heat pump systems;on assessment of the scenario clustering in stochastic bidding: a full-cycle perspective;energy consumption management in buildings in the context of voluntary and mandatory demand response programs in smart grids;supplementary damping control design for large scale pv power plant at transmission level interconnection;reinforcement learning based energy management algorithm for energy trading and contingency reserve application in a microgrid;data driven framework for load profile generation in medium voltage networks via transfer learning;and impact of unbalanced electric vehicle charging on low-voltage grids.

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data-driven Secure Authentication for Smart Grid IoT Networks 9

Data-driven Secure Authentication for Smart Grid IoT Network...

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9th ieee International conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Singhal, Saumyashree Tripathi, Sharda Birla Institute of Technology and Science Pilani Pilani India

ISBN: (纸本)9798350334395

Internet of things (IoT) has found wide-spread usage in cyber physical systems. We consider the security aspects related to cyber physical systems, particularly the smart grid, wherein IoT devices are deployed for monitoring and control, often in the open without any active surveillance, thereby increasing their vulnerability to security attacks. To this end, device authentication is an essential security feature for smart grid IoT networks. A desirable authentication protocol designed for IoT devices should not only be robust when the device is compromised by an adversary, it also needs to be computationally efficient in the wake of limited storage and computational capabilities of IoT devices. In this work, we address the critical issue of device security in smart grid IoT networks, and propose a data-driven, yet lightweight and privacy preserving authentication scheme for IoT devices. the performance analysis based on our experiments show that the proposed scheme is robust against heterogeneous network attacks, and significantly reduces computational load. © 2023 ieee.

关键词： Machine learning

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Big data Mining Method of thermal Power Based on Spark and Optimization Guidance

Big Data Mining Method of Thermal Power Based on Spark and O...

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data driven control and learning systems (ddcls)

作者： Mingcheng Song Li Jia Department of Automation College of Mechatronics Engineering and Automation Shanghai University Shanghai P.R. China Shanghai Key Laboratory of Power Station Automation Technology Department of Automation College of Mechatronics Engineering and Automation Shanghai University Shanghai P. R. China

ISBN: (纸本)9781538626191

With the increasing degree of information technology in the electric-power industry, the amount of big data in thermal power has increased geometrically. To address the problem of the computational bottlenecks in traditional data mining deal with big data of thermal power, big data mining of thermal power method based on Spark is presented in this paper. According to the characteristics of the actual operation of the unit, the proposed method determines the steady-state conditions of big data of thermal power and divides the working conditions based on external constraints. In addition, data mining method based on distributed computing is used to mine big data of thermal power to get the strong association rules, thus the best value of the parameters under each working condition can be got. Lastly, the historical knowledge base is established, which can guide the operation of the unit by the proposed method. this method is applied to a 300 MW unit in a power plant in Anhui Province, and mines the operation data of the unit for 10 days in a month. the results of simulation show that the proposed method can effectively mine big data of thermal power and has the advantage of computational efficiency compared with traditional data mining for big data.

关键词： Big data data mining Sparks Employee welfare Indexes Optimization Knowledge based systems

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