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Content style-triggered Backdoor Attack in Non-IID Federated learning via Generative AI 21

Content Style-triggered Backdoor Attack in Non-IID Federated...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Cheng, Jinke Li, Gaolei Lin, Xi Peng, Hao Li, Jianhua Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering China Zhejiang Normal University Computer and Information Sciences China

ISBN: (纸本)9798350329223

Federated learning (FL) enables collaborative model training over multiple unvetted participants' data, making it vulnerable to backdoor attacks. While existing studies have presented many efficient methods to improve the persistence and stealthiness of those attacks, the assumption about the configurable space of adversaries may still be too conservative, as attackers may utilize any tool (e.g., Generative AI) to generate poisoned samples, especially under non-IID scenarios, where not every client has data for all styles. In this paper, we warn that the generative AI technique can be weaponized to produce poisoned samples, which is essential to implant hidden backdoors into the FL-based systems. Specifically, a novel content style-triggered backdoor attack (CSBA) scheme is designated to raise up more attention. The CSBA mainly consists of two processes: 1) style transferring-based poisoned data generation and 2) federated backdoor implantation. In the former stage, we use CycleGAN and stable Diffusion to construct poisoned samples with specific styles, respectively, and mark them as predefined target labels. For the latter, a standard federated backdoor implantation process is executed and ultimately achieves a backdoored model that only is sensitive to inputs with a specific style. Experiments based on CIFAR10 and ImageNet datasets validate the effectiveness of the proposed methods. © 2023 IEEE.

关键词： Backdoor attack Federated learning Generated AI Non-IID style transfer

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IoT-Enabled intelligent Farming System with Integrated Tracking and Water Gathering Plan

IoT-Enabled Intelligent Farming System with Integrated Track...

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2023 international conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Bhargav, Mekala Sai, Nariboyina Pavan Pavan, N. Gracewell, Jeffin Saveetha Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350334357

The goal of this research is to develop a smart irrigation system based on the Internet of Things that is affordable for a typical middle-class farmer. In the 21st century, automation has been more integrated into daily life. As a result of automation, machines may be programmed to operate without human intervention. The reduced energy use, increased productivity, and decreased amount of time spent on mundane tasks are all bonuses. In addition to being crucial to people's ability to eat and stay alive, agriculture is critical to a nation's economic development and overall prosperity. With the help of innovations like the Internet of Things (IoT), wireless sensor networks (WSNs), remote sensing, drone surveillance, and more, 'fastidious agriculture' has emerged as a hot topic in the IT industry. In this study, we demonstrate the efficacy of our gadget, which is designed to give data and save water by only watering fields when they really need it. Cloud website named item talk is used to transmit the data to the farmers. The farmers may keep tabs on their fields with the use of thing speaks channel connection. A built-in Wi-Fi module in the microcontroller transmits the collected data to the thing talk cloud storage service. Then, we'll evaluate how much water is saved in various soil types, such as sand, clay, and sandy clay. The water savings from this technology are clearly visible when compared to the water savings from the conventional technique. © 2023 IEEE.

关键词： Internet of things

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Using Automatic Programming to Improve Gradient Boosting for Classification 21st

Using Automatic Programming to Improve Gradient Boosting for...

引用

21st international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Olsson, Roland Acharya, Shubodha Ostfold Univ Coll Halden Ostfold Norway

ISBN: (纸本)9783031234910;9783031234927

In this paper, we present our new and automatically tuned gradient boosting software, Classifium GB, which beats its closest competitor, H2O, for all datasets that we ran. The primary reason that we found it easy to develop Classifium GB is that we employed meta machine learning, based on evolution, to automatically program its most important parts. Gradient boosting is often the most accurate classification algorithm for tabular data and quite popular in machine learning competitions. However, its practical use has been hampered by the need to skilfully tune many hyperparameters in order to achieve the best accuracy. Classifium GB contains novel regularization methods and has automatic tuning of all regularization parameters. We show that Classifium GB gives better accuracy than another automatically tuned algorithm, H2O, and often also outperforms manually tuned algorithms such as XGBoost, LightGBM and CatBoost even if the tuning of these is done with exceptional care and uses huge computational resources. Thus, our new Classifium GB algorithm should rapidly become the preferred choice for practically any tabular dataset. It is quite easy to use and even say Random Forest or C5.0 require more skilled users. The primary disadvantage is longer run time.

关键词： Machine learning Gradient boosting XGBoost LightGBM CatBoost AutoML Hyperparameters Automatic programming Automatic design of algorithms through evolution Meta machine learning

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learning the automated Setup of Profile Wrapping Lines for New Products from Few Past Setups

Learning the Automated Setup of Profile Wrapping Lines for N...

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IEEE international conference on Industrial Informatics (INDIN)

作者： steven Koppert Maximilian Bause Christian Henke Ansgar Trächtler Scientific Automation Fraunhofer Institute for Mechatronic Systems Design Paderborn Germany Control Engineering and Mechatronics Heinz Nixdorf Institute Paderborn Germany

This study investigates the feasibility of automated setup of profile wrapping processes on new products using machine learning on past setup examples. The task is characterized by high complexity of the considered production system in combination with highly varying products and a very small available database. This database also reveals ambiguous ground truth due to human, unsystematic preferences. A simple geometric-physical motivated preprocessing is proposed. On the resulting data, a Deep Convolutional Neural Network in the form of an autoencoder is shown to be very suitable for predicting wrapping actions for new products. The good but improvable results are discussed extensively with respect to the technological background and possible solutions are proposed.

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data-Driven Divide-and-Conquer for Estimating Build Times of 3D Objects 21

Data-Driven Divide-and-Conquer for Estimating Build Times of...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Tabassian, Mahdi Verbeke, Robbert Tourwe, Tom Tsiporkova, Elena EluciDATA Lab Sirris Brussels Belgium

ISBN: (纸本)9781665424271

Accurate estimation of build times of 3D objects is of great importance in the different phases of an additive manufacturing process. The existing physics-based models can very precisely tackle this task but at the cost of spending a considerable amount of time. An alternative solution is to use a data-driven machine learning method for build time estimation (BTE). However, estimating build times of a dataset of objects with diverse and heterogeneous characteristics is a challenging task for a single learning algorithm. The aim of this study is therefore to investigate the value of the divide-and-conquer strategy in partitioning the dataset into subsets of homogeneous objects to facilitate the BTE task for the examined learning models. The comprehensive experiments performed in this study prove that this strategy is indeed capable of providing accurate BTEs and can outperform the performance of a single learning method trained with all objects in the dataset.

关键词： additive manufacturing machine learning feature engineering and selection divide-and-conquer

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Bearing Failure Classification with Metaheuristic Adjust of the Extreme learning Machine Model

Bearing Failure Classification with Metaheuristic Adjust of ...

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international conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Luis Elias Salgado Solano Oliverio Arellano Cárdenas Luis Martín Flores Nava Felipe Gómez Castaneda Electrical Engineering Department CINVESTAV-IPN Mexico City Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

In this study, we propose the use of an Extreme learning Machine model with a metaheuristic adjust of parameters, to recognize mechanical vibration data from bearings, in order to determine whether the device is in good condition or experiencing some type of malfunction. Our approach involved designing a supervised Extreme learning Machine model whose parameters are adjusted by a Grey Wolf approach. We employed a dataset from Case Western Reserve University, available on their website. In this database the signals were gathered by sampling an accelerometer at 12 kS/s, placed radially on a bearing. The data was collected for four scenarios: one with vibrational signals from a healthy bearing, and three with bearings having induced faults in the inner race, outer race, and ball, respectively. This intelligent system was developed using Python in the Spyder environment, integrated with Pytorch framework, obtaining promising results. Keywords-Extreme learning Machine, metaheuristics, bearing failures, artificial intelligence.

关键词： Vibrations Extreme learning machines databases Metaheuristics Neural networks learning (artificial intelligence) Electrical engineering computing data models Classification algorithms intelligent systems

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Research on the Application of LstM Model Based on Electrical Equipment in Electromagnetic Field Calculation

Research on the Application of LSTM Model Based on Electrica...

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2022 international conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

作者： Chen, Zipeng School of Communication and Electronic Engineering East China Normal University Shanghai201101 China

ISBN: (纸本)9781665462877

Since the 21st century, the development of information technology and artificial intelligence has reached new heights, and the intelligent manufacture of electrical equipment has become the main development goal of industrial manufacturing in China. The core part of intelligent manufacturing of electrical equipment is the use of data to build mathematical models for real-Time analysis, but for large volume and complex structure of electrical equipment, the traditional finite element analysis method will involve large-scale numerical calculations, resulting in slow calculation speed, accuracy is difficult to meet the design requirements, cannot meet the real-Time requirements. This paper applies deep learning theory to the electromagnetic field analysis of electric motors, using the electromagnetic field distribution data corresponding to different motor structures to train the built deep learning model to predict the electromagnetic field distribution instead of the traditional finite element calculation, improving research efficiency, reducing time costs and meeting the requirements of intelligent manufacturing for real-Time. In this paper, the predicted electromagnetic field distribution is compared with the MSE and MAE of the training set and test set respectively, then the electromagnetic field distribution data is plotted as a line graph to observe the error between the true and predicted values, and finally the predicted electromagnetic field distribution data is visualised as a magnetic line graph using simulation software, and compared with the magnetic line graph obtained from ANSYS Maxwell simulation, and the data of some random points are taken for comparison. © 2022 IEEE.

关键词： Finite element method

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A Few-shot learning Approach for Anomaly Detection in Cooperative Tracking Control System 21

A Few-shot Learning Approach for Anomaly Detection in Cooper...

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作者： Rong, Jieqi Liu, Weirong Yu, Xiaoquan Ren, Wanwan Shu, Boyu Peng, Jun Central South University Computer Science and Engineering Changsha China Central South University School of Traffic and Transportation Engineering Changsha China Central South University School of Automation Changsha China

ISBN: (纸本)9798350329223

Cooperative tracking control for multiple heavy-haul trains can improve the safety and efficiency of heavy-haul railway transportation. However, the false data injection attack for the tracking control system is a serious obstacle, which will lead to imprecise train operation control. To address the issue, a few-shot learning approach for false data injection attack (FDIA) detection for tracking control systems of heavy-haul trains is proposed. First, the physical-cyber model of the tracking control system is established, and the affects of the FDIA on the tracking control system is analyzed. Second, the heavy-haul train anomaly detection simulation model was built by Matlab/Simulink to obtain the dataset for training the GAN model. Third, the Generative Adversarial Network (GAN) method is introduced to augment the anomaly dataset, which can solve the problem of lack of actual operation fault data of heavy trains. Further, the Local Outlier Factor (LOF) algorithm is leveraged to achieve the false data injection attack detection. Simulation results show that the proposed method can effectively increase the anomaly detection accuracy than other supervised methods. © 2023 IEEE.

关键词： Anomaly detection

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Occupancy Detection based on WI-FI SysLog data 21

Occupancy Detection based on WI-FI SysLog Data

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21st IEEE international conference on Machine learning and Applications (IEEE ICMLA)

作者： Abdelghani, Bassam A. Maleki, Mina Banitaan, Shadi Mazen, Amna Univ Detroit Mercy Dept Elect & Comp Engn & Comp Sci Detroit MI 48221 USA

ISBN: (纸本)9781665462839

Residential and commercial buildings consume almost 27% of the total energy in the United states. Therefore, accurate occupancy detection is necessary and valuable to increase the efficiency of the building's Heating, Ventilation, and Air Conditioning (HVAC) system while maintaining the occupants' comfort. The occupancy detection process involves deploying two fundamental types of sensors - environmental sensors (humidity, carbon dioxide, temperature, and light sensors) and specialized sensors (Passive infrared, Radio Frequency Identification sensors, cameras, and WI-FI). These sensors are used with three primary categories of algorithms - analytical, knowledge-based, and datadriven. Our paper uses Syslog data from a university building network to generate eight different datasets after removing nonessential data and retaining only those needed. A framework is then proposed, which consists of two stages. The first stage compares the results of four machine learning algorithms, including K-Nearest Neighbors (KNN), Random Forest (RF), eXtreme Gradient Boosting (XGB), and Logistic Regression (LR), to choose the best two models. In this work, XGB and RF produce the most accurate results. The second stage tests the best two models on eight datasets composed of the whole year dataset, four semester-based datasets, and three season-based datasets. In this work, the semester-based dataset gives the most accurate results with a Root Mean Square Error (RMSE) of 2.51 and the highest coefficient of determination (R-2) of 0.48.

关键词： Occupancy Detection WI-FI HVAC Random Forest XGB

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Link load balancing scheme for elephant flow in SDN data center 21

Link load balancing scheme for elephant flow in SDN data cen...

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作者： Hao, Feifan Jing, Shan Zhao, Chuan University of Jinan School of Information Science and Engineering Jinan China Quan Cheng Laboratory Jinan China

ISBN: (纸本)9798350329223

In recent years, the rapid emergence of Internet industries such as big data and cloud computing has led to significant growth in data centers. The integration of Software Defined Network(SDN) technology with data centers empowers the controller to manage the network globally and regulate traffic forwarding. This seamless integration significantly enhances the overall operational efficiency of data centers. Nevertheless, as data center traffic surges, a substantial volume of traffic floods the links, potentially leading to load imbalances and severe link congestion under traditional traffic scheduling modes. The concept of 'mice flow and elephant flow' introduces a novel idea to enhance load balancing in data centers. In this paper, we propose a novel data center link load balancing method called EDDQN (Elephant Detection Deep Q-learning Network). EDDQN involves a dynamic, two-stage detection process for identifying elephant flow at the edge. Subsequently, the DQN algorithm is utilized to reroute the detected elephant flow, optimizing the load distribution in the data center network. With the advantages of SDN, the link utilization and throughput of data center networks can be improved. Additionally, the packet loss rate and transmission delay can be reduced, thereby improving the network performance. © 2023 IEEE.

关键词： Reinforcement learning

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