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检索条件"任意字段=Conference on Internet Performance and Control of Network Systems II"

共 4637 条记录，以下是151-160 订阅

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performance Analysis of Universal Robot control System Using networked Predictive control 7

Performance Analysis of Universal Robot Control System Using...

引用

7th International conference on Robotics and Automation Engineering (ICRAE)

作者： Noroozi, Mahsa Kies, Lorenz Leibniz Univ Hannover Inst Commun Technol Hannover Germany

ISBN: (纸本)9781665489188

networked control systems are feedback control systems with system components distributed at different locations connected through a communication network. Since the communication network is carried out through the internet and there are bandwidth and packet size limitations, network constraints appear. Some of these constraints are time delay and packet loss. These network limitations can degrade the performance and even destabilize the system. To overcome the adverse effect of these communication constraints, various approaches have been developed, among which a representative one is networked predictive control. This approach proposes a controller, which compensates for the network time delay and packet loss actively. This paper aims at implementing a networked predictive control system for controlling a robot arm through a computer network. The network delay is accounted for by a predictor, while the potential of packet loss is mitigated using redundant control packets. The results will show the stability of the system despite a high delay and a considerable packet loss. Additionally, improvements to previous networked predictive control systems will be suggested and an increase in performance can be shown. Lastly, the effects of different system and environment parameters on the control loop will be investigated.

关键词： networked predictive control systems time delay packet loss

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Lateral control for Autonomous Vehicles Utilizing an ANN-Based controller 10

Lateral Control for Autonomous Vehicles Utilizing an ANN-Bas...

引用

10th International conference on Optimization and Applications, ICOA 2024

作者： Elouardi, Elhoussein Sellak, Lahcen El Mokhi, Chakib Adnani, Younes Idkhajine, Lahoucine El Ouardi, Brahim Mouhib, Omar Ibn Tofail University Setime Laboratory Faculty of Sciences Kenitra Morocco National School of Applied Sciences Ibn Zohr University Lisad Research Team Industrial Engineering Department Agadir Morocco Higher School of Technology Ibn Tofail University Kénitra Morocco Cy Cergy-Paris University Satie Laboratory Cergy-Pontoise95031 France Hassan Ii University LPMC-ERSA Faculty of Sciences Ben M'sik Department of Physics Casablanca Morocco

ISBN: (纸本)9798350387353

This article investigates the use of artificial intelligence, particularly artificial neural networks (ANNs), to enhance road safety by refining lateral stability and trajectory tracking in autonomous driving systems. ANNs are utilized as control models to optimize the performance of these safety systems. The study employs rigorous methodologies for data collection, neural network training, and thorough testing and validation to evaluate the efficacy of these controllers. The primary goal is to compare the performance of ANN-based controllers with that of established automotive simulation software like Carsim, focusing specifically on metrics related to stability and efficiency. Numerical simulations confirm the validity and effectiveness of the proposed methods, showcasing how AI-driven control systems can significantly improve road safety in autonomous driving scenarios. © 2024 IEEE.

关键词： ANN-Based controller Lateral control self-driving vehicle dynamics

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iioT network Intrusion Detection Using Machine Learning 6

IIoT Network Intrusion Detection Using Machine Learning

引用

6th International conference on Intelligent Robotics and control Engineering, IRCE 2023

作者： Eid, Abdulrahman Mahmoud Nassif, Ali Bou Soudan, Bassel Injadat, Mohammad Noor University of Sharjah Computer Engineering Dept. Sharjah United Arab Emirates Zarqa University Computer Science Dept. Zarqa Jordan

ISBN: (纸本)9798350326338

Industrial internet of Things (iioT) systems are subject to intrusion attacks that may cause devastating damage. However, common security mechanisms may not be applicable as iioT systems and standard IT networks are fundamentally different. Therefore, it is necessary to consider different security factors and performance metrics when addressing the vulnerabilities and security demands of iioT. Machine Learning (ML) has proven to be an effective tool in the development of security mechanisms and Intrusion Detection systems (IDS) for iioT. This paper presents the implementation of multiple IDS models using six ML algorithms. These models were evaluated on the WUSTL-iioT-2021 dataset, and their performance was analyzed and compared. Among the six models, the model constructed using the Random Forest (RF) algorithm achieved an accuracy of 99.97%, which is significantly higher than the accuracy achieved by previously reported models in the literature. The results demonstrate the significance of this work and highlight the potential of ML-based IDS models in ensuring the security and safety of iioT systems. © 2023 IEEE.

关键词： internet of things

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Towards a Supervised Machine Learning Algorithm for Cyberattacks Detection and Prevention in a Smart Grid Cybersecurity System 2nd

Towards a Supervised Machine Learning Algorithm for Cyberatt...

引用

2nd Pan-African conference on Artificial Intelligence (PanAfriCon AI)

作者： Banda, Takudzwa Vincent Blaauw, Dewald Watson, Bruce W. Stellenbosch Univ Ctr AI Res CAIR Sch Data Sci & Computat Thinking Stellenbosch South Africa Stellenbosch Univ Dept Informat Sci Stellenbosch South Africa

ISBN: (纸本)9783031576386;9783031576393

Critical infrastructure cyberattacks have become a significant threat to national security worldwide. Adversaries exploit vulnerabilities in communication networks, technologies, and protocols of smart grid control systems network to gain access and control of power grids, causing blackouts. Despite the need to safeguard the reliable and stable operation of the grid against cyberattacks, simultaneously detecting and preventing attacks presents a significant challenge. To address this, a Kali Linux machine was connected to a smart grid control system network emulated in GNS3 to perform common cyberattacks. Wireshark was then deployed to capture network traffic for machine learning. Aiming to improve the detection and prevention of cyberattacks the study proposed a dual-tasked ensemble supervised machine learning model, a combination of Neural network and Extreme Gradient Boosting, that had an average accuracy of 99.60% and detection rate of 99.48%. The first task of the model distinguishes between normal state and cyberattack modes of operation. The second task prevents suspicious packets from reaching the network destination devices. Leveraging the PowerShell Script, the model dynamically applies packet filtering firewall rules based on its predictions. The proposed model was tested on new data, producing an accuracy of 99.19% and a detection rate of 98.95%. Furthermore, the model's performance was compared to existing proposed cyberattack detection models. Thus, the proposed model, with its function as a firewall, enhances the overall security capabilities of the smart grid and significantly mitigates potential cyberattacks.

关键词： Smart Grid Supervised Machine Learning control System Cybersecurity Cyberattacks Neural network XGBoost Firewall network Traffic

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Reinforcement Learning-Based Cooperative Traffic control System 16th

Reinforcement Learning-Based Cooperative Traffic Control Sys...

引用

16th International conference on Computational Collective Intelligence (ICCCI)

作者： Barta, Zoltan Kovacs, Szilard Botzheim, Janos Eotvos Lorand Univ Fac Informat Dept Artificial Intelligence Pazmany Peter Setany 1-A H-1117 Budapest Hungary

ISBN: (纸本)9783031708183;9783031708190

Urban traffic congestion is an increasingly pressing issue and advanced solutions like intelligent traffic control systems are becoming unavoidable. This paper explores the application of reinforcement learning to enhance traffic flow and reduce congestion. Our goal was to develop a reinforcement learning-based model that adapts to varying traffic conditions in real-time. Several methods are available for the real-time optimization of traffic, ranging from analytical methods to more flexible metaheuristic methods and reinforcement learning-based solutions. Each method lacks either adaptivity or scalability, or the completeness of the global optimization, or the performance requirement is too high. Based on the different requirements it is clearly a challenging task. The focus of our research is on the scalability, and computational efficiency of the model by using a method of sharing information that is similar to a cellular network. Our solution is not just easy to scale but also able to search for the global optimum with a low computational cost. A general model was trained to achieve out-of-the-box usage capability. The control network also can be finetuned for better performance. Our comprehensive analysis showed that smart traffic lights significantly enhance the efficiency of traffic systems, boasting improvements ranging from 10% to 80% compared to traditional and other reinforcement learning-based solutions. These intelligent controllers not only reduce waiting times but also contribute to environmental protection by reducing the carbon footprint.

关键词： traffic control multi-agent reinforcement learning scalable and efficient optimization incremental learning smart city

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Adaptive Security in a Connected World: Review of Machine Learning for IoT Intrusion Detection 8

Adaptive Security in a Connected World: Review of Machine Le...

引用

8th IEEE International conference on Computing, Communication, control and Automation, ICCUBEA 2024

作者： Mer, Hiren V. Suthar, Anil Gujarat Technological University Ahmedabad India New Lj Institute of Technology & Engineering Ahmedabad India

ISBN: (纸本)9798350391770

In IoT Security risks are, however, presented to devices and services by this integration. To give machine learning (ML) credit for being a novel approach, this paper surveys Intrusion Detection systems (IDS) for the Traditional network and internet of Things. With 41 articles review, Machine Learning can adapt to the changing IoT environment, Deep Learning (DL) in particular shows promise as a method. IoT device with security problem with complex IoT landscape is too much for traditional rule-based IDS to handle, which has led to a rise in interest in machine learning approaches. To solve issues used different techniques are investigated. The research discusses important feature selection techniques related to IoT intrusion detection, emphasizing the value of feature engineering and classification. With newly created datasets both normal and malicious scenarios use performance measures such as F1-score, accuracy, precision, recall, and AUC-ROC. © 2024 IEEE.

关键词： network intrusion

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Collaborative Access control for IoT - A Blockchain Approach 3

Collaborative Access Control for IoT - A Blockchain Approach

引用

3rd IEEE International conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Huang, Yongtao Yen, I-Ling Bastani, Farokh University of Texas Dallas United States

ISBN: (纸本)9798350354348

The internet of Things (IoT) necessitates robust access control mechanisms to secure a vast array of interconnected devices. We adopt the blockchain based decentralized access control approach and identify the gaps in existing works. Then, We develop a blockchain embedded access control (BEAC) framework to bridge the gaps. First, blockchain based solutions for access control require an enabling P2P network while existing P2P overlays do not support some required features. We develop a novel P2P infrastructure to seamlessly support our BEAC framework. Second, most of the works consider blockchain based access control for a single access control model, and we develop a generic blockchain mechanism and show that it can support the embedding of various access control models. Finally, existing works adopt existing blockchain platforms which may incur a high communication overhead. We design a more efficient consortium blockchain solution by leveraging more advanced components to achieve better performance. Our experiments demonstrate that our design can cut the communication cost to half compared to the most recent Hyperledger Fabric platform. © 2024 IEEE.

关键词： Decentralized control

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performance Analysis of mdx ii: A Next-Generation Cloud Platform for Cross-Disciplinary Data Science Research 15

Performance Analysis of mdx II: A Next-Generation Cloud Plat...

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15th International conference on Cloud Computing and Services Science, CLOSER 2025

作者： Takahashi, Keichi Hayami, Tomonori Mukaizono, Yu Teramae, Yuki Date, Susumu D3 Center University of Osaka Osaka Japan

ISBN: (纸本)9789897587474

mdx ii is an Infrastructure-as-a-Service (IaaS) cloud platform designed to accelerate data science research and foster cross-disciplinary collaborations among universities and research institutions in Japan. Unlike traditional high-performance computing systems, mdx ii leverages OpenStack to provide customizable and isolated computing environments consisting of virtual machines, virtual networks, and advanced storage. This paper presents a comprehensive performance evaluation of mdx ii, including a comparison to Amazon Web Services (AWS). We evaluated the performance of a 16-vCPU VM from multiple aspects including floating-point computing performance, memory throughput, network throughput, file system and object storage performance, and real-world application performance. Compared to an AWS 16-vCPU instance, the results indicated that mdx ii outperforms AWS in many aspects and demonstrated that mdx ii holds significant promise for high-performance data analytics (HPDA) workloads. We also evaluated the virtualization overhead using a 224-vCPU VM occupying an entire host. The results suggested that the virtualization overhead is minimal for compute-intensive benchmarks, while memory-intensive benchmarks experienced larger overheads. These findings are expected to help users of mdx ii to obtain high performance for their data science workloads and offer insights to the designers of future data-centric cloud platforms. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Data Science

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Strengthening network Intrusion Detection in IoT Environments with Self-supervised Learning and Few Shot Learning 16th

Strengthening Network Intrusion Detection in IoT Environment...

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16th International conference on Computational Collective Intelligence (ICCCI)

作者： Ben Atitallah, Safa Driss, Maha Boulila, Wadii Koubaa, Anis Prince Sultan Univ RIOTU Lab CCIS Riyadh 12435 Saudi Arabia Univ Manouba RIADI Lab Manouba 2010 Tunisia

ISBN: (纸本)9783031708183;9783031708190

The internet of Things (IoT) has been introduced as a breakthrough technology that integrates intelligence into everyday objects, enabling high levels of connectivity between them. As the IoT networks grow and expand, they become more susceptible to cybersecurity attacks. A significant challenge in current intrusion detection systems for IoT includes handling imbalanced datasets where labeled data are scarce, particularly for new and rare types of cyber attacks. Existing literature often fails to detect such underrepresented attack classes. This paper introduces a novel intrusion detection approach designed to address these challenges. By integrating Self Supervised Learning (SSL), Few Shot Learning (FSL), and Random Forest (RF), our approach excels in learning from limited and imbalanced data and enhancing detection capabilities. The approach starts with a Deep Infomax model trained to extract key features from the dataset. These features are then fed into a prototypical network to generate discriminate embedding. Subsequently, an RF classifier is employed to detect and classify potential malware, including a range of attacks that are frequently observed in IoT networks. The proposed approach was evaluated through two different datasets, MaleVis and WSN-DS, which demonstrate its superior performance with accuracies of 98.60% and 99.56%, precisions of 98.79% and 99.56%, recalls of 98.60% and 99.56%, and F1-scores of 98.63% and 99.56%, respectively.

关键词： Self Supervised Learning Few Shot Learning Intrusion Detection and Classification internet of Things

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Remote Monitoring and control of Battery Management System using CAN Communication and IoT Device for Cloud Based Analysis and Management 7

Remote Monitoring and Control of Battery Management System u...

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7th IEEE conference on Information and Communication Technology, CICT 2023

作者： Earthineni, Nirupam Sivavenkateswara Rao, V. Indian Institute of Information Technology Electronics and Communication Engineering Surat India

ISBN: (纸本)9798350305173

In this research article, two methods suitable for remote monitoring and control of battery management system (BMS), respectively are proposed. The methods use controller area network (CAN) communication and internet of things (IoT) device for cloud-based analysis and management. The proposed method has several advantages over traditional onboard BMS systems, such as remote monitoring and control, scalability, analytics, and flexibility. Additionally, it facilitate various features like optimized battery performance, reduce maintenance costs, and improve safety and reliability in electric vehicles and other applications. With the necessary hardware and software components, the proposed methods are tested, and the results are presented. © 2023 IEEE.

关键词： internet of things

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