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97th IEEE Vehicular Technology conference (VTC-Spring)

作者： Kryszkiewicz, Pawel Hoffmann, Marcin Poznan Univ Tech Inst Radiocommunicat Rimedo Labs Poznan Poland

ISBN: (纸本)9798350311143

One of the essential security threats for 5G networks is jamming. It is relatively easy to be performed utilizing cheap and publically available devices. Open Radio Access network (O-RAN) architecture is very suitable for detecting such events thanks to the openness of interfaces and the ability to analyze wireless traffic metrics and exchange control messages in a RAN Intelligent controller (RIC) using some dedicated xApp or rApp. This paper presents a statistical method for downlink jamming detection utilizing the link quality reports provided by User Equipments (UEs). A vision of its implementation in O-RAN is presented altogether with performance quality metrics obtained via simulations.

关键词： 5G mobile communication systems

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Wireless Sensor network Hop Count Theory Modeling and Deep Learning Prediction 4

Wireless Sensor Network Hop Count Theory Modeling and Deep L...

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4th IEEE International Maghreb Meeting of the conference on Sciences and Techniques of Automatic control and Computer Engineering, MI-STA 2024

作者： Sati, Salem Omar Abugharsa, Afif Misurata University Information Technology Faculty Libya

ISBN: (纸本)9798350372632

Wireless Sensor networks (WSNs) are utilized as distributed systems for wireless communication and data collection from sensors. A WSN can be represented as a graph consisting of sensors and wireless links. If this graph has a path connecting any source sensor to any destination sensor, it is considered a connected network graph. Otherwise, it is a disconnected graph. The connectivity of a network graph is crucial for its proper functioning and applications. network connectivity can be affected if a certain number of sensor nodes are missing from the topology. This number of nodes can be used as a threshold for connectivity. In a WSN, sensors communicate with their direct neighbors based on the quality of their communication links. This paper proposes a mathematical model to address connectivity and link probability issues in WSNs. The model examines the impact of varying link probability and the number of deployed sensors on network connectivity. The model is evaluated using different radio models in the Cooja emulator with the Rime stack. A multi-hop WSN is implemented as a linear path with various radio models to analyze network connectivity probability and estimate the maximum hop count. Additionally, the success of flood route estimation based on link probability is investigated. network performance metrics such as packet delivery ratio and packet loss rate are used to evaluate network connectivity and measure the maximum hop count for both radio models. Furthermore, the collected simulation dataset is employed in Deep Learning models to predict the hop count based on link quality parameters. © 2024 IEEE.

关键词： Sensor nodes

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ClassDegSR: A Fast network for Degraded Image Super Resolution Based on Degrade Degree Classification 35

ClassDegSR: A Fast Network for Degraded Image Super Resoluti...

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35th Chinese control and Decision conference (CCDC)

作者： Yan, Weidan Chen, Can Ying, Junhao Feng, Yingying Du, Qunjian Zhang, Dengyin Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Peoples R China

ISBN: (纸本)9798350334722

Image degradation, which is caused by imaging devices instability, brings great challenges to single image super resolution (SISR). However, the existing degraded image super resolution (DegSR) networks reconstruct all image blocks via one complex network, which ignore degradation degree of image blocks and result in low reconstruction efficiency. Therefore, this paper proposes a fast network for DegSR based on degrade degree classification, dubbed ClassDegSR. Specifically, image blocks are classified based on degradation degree via a Class- Module, which are further processed by different scale branches via a DegSR-Module. Moreover, a three-stage training strategy which utilizes pretrained modules is proposed to enhance the training robustness. According to simulation results, ClassDegSR effectively improves reconstruction quality and efficiency, which has practical value in systems with tight computing resources. And the code is available at https://***/vanwd628/ClassDegSR.

关键词： DegSR Degrade Degree Classification fast network

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Zadeh's Type-2 Fuzzy Logic systems: Precision and High-quality Prediction Intervals

Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quali...

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IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Guven, Yusuf Koklu, Ata Kumbasar, Tufan Istanbul Tech Univ Control & Automat Engn Dept Istanbul Turkiye

ISBN: (纸本)9798350319552;9798350319545

General Type-2 (GT2) Fuzzy Logic systems (FLSs) are perfect candidates to quantify uncertainty, which is crucial for informed decisions in high-risk tasks, as they are powerful tools in representing uncertainty. In this paper, we travel back in time to provide a new look at GT2-FLSs by adopting Zadeh's (Z) GT2 Fuzzy Set (FS) definition, intending to learn GT2-FLSs that are capable of achieving reliable High-quality Prediction Intervals (HQ-PI) alongside precision. By integrating Z-GT2-FS with the a-plane representation, we show that the design flexibility of GT2-FLS is increased as it takes away the dependency of the secondary membership function from the primary membership function. After detailing the construction of Z-GT2-FLSs, we provide solutions to challenges while learning from high-dimensional data: the curse of dimensionality, and integrating Deep Learning (DL) optimizers. We develop a DL framework for learning dual-focused Z-GT2-FLSs with high performances. Our study includes statistical analyses, highlighting that the Z-GT2-FLS not only exhibits high-precision performance but also produces HQ-PIs in comparison to its GT2 and IT2 fuzzy counterparts which have more learnable parameters. The results show that the Z-GT2-FLS has a huge potential in uncertainty quantification.

关键词： general type-2 fuzzy sets design flexibility accuracy uncertainty prediction interval deep learning

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IoT Security: Cost-Effective Solution for Detecting Multiple Attacks in IoT network Using Machine Learning 1st

IoT Security: Cost-Effective Solution for Detecting Multiple...

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1st International conference on Electrical systems and Smart Technologies, ICESST 2024

作者： Rouagubi, Abdelkabir Benmalek, Elmehdi Enassiri, Omar Engineering Sciences Laboratory National School of Applied Sciences Ibn Tofail University Kenitra Morocco ENSAM de Rabat Mohammed V University of Rabat Rabat Morocco

ISBN: (纸本)9783031867040

The exponential expansion of the internet of Things (IoT) has resulted in a heightened level of automation and connectedness among gadgets, eliminating the need for deliberate user involvement, thereby augmenting the overall quality of our lives. Nevertheless, the security of IoT devices is a paramount issue since they are susceptible to cyber-attacks, which can result in substantial harm if not promptly identified and addressed. The present work introduces a methodology that combines three machine learning classifiers with principal component analysis (PCA) to efficiently and precisely identify IoT network attacks. The suggested strategy is evaluated using the Bot-IoT dataset, and the observed findings demonstrate substantial efficiency in detection performance when compared to current methodologies that use the whole features vector. In the evaluation, the DNN algorithm achieved an 88.6% f1-score, while the SVM and Decision tree reached 87.21% and 80.58% respectively. In contrast, the accuracy of the other ML algorithms did not fall below 95%. The proposed methodology can be further expanded to augment the security of other internet of Things (IoT) applications, therefore presenting a prospective addition to the domain of IoT security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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Optimal denial-of-service attack scheduling for remote state estimation via sequential observations 43

Optimal denial-of-service attack scheduling for remote state...

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43rd Chinese control conference, CCC 2024

作者： Yang, Xinxin Ni, Yuqing Chen, Xiaomeng Chen, Shan Liu, Fei Jiangnan University The Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Engineering Wuxi214122 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong999077 Hong Kong

ISBN: (纸本)9789887581581

This study investigates the optimal allocation problem of attack energy in multi-system remote state estimation for Cyber-Physical systems (CPSs). We consider a scenario where there are M independent systems, each equipped with a remote sensor monitoring system. The local estimates from these systems are sent to a remote estimator through a packet loss channel. The objective of the attacker is to degrade the communication channel between the sensors and the remote estimator. Due to capacity limitations, the attacker is restricted to attacking at most one out of M channels. We incorporate interference from other sensors or interference sources into the communication model based on the SINR (Signal-to-Interference-plus-Noise Ratio) model. It should be noted that the same attack power can lead to different packet loss rates in different network environments. From the attacker's perspective, the goal is to find the optimal attack strategy that maximizes the average estimation error of the remote estimator. Given the uncertainties of wireless network environments, we employ the sequentially-observed Markov decision process (SOMDP) framework to solve the problem. By observing the wireless channels, we gather additional information and incorporate it into the decision-making policy, resulting in better performance than using a standard Markov decision process (MDP). The simulation results support our theoretical findings. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Markov processes

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Proceedings - 2024 2nd International conference on Intelligent control and Computing, IC-C 2024

Proceedings - 2024 2nd International Conference on Intellige...

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2nd International conference on Intelligent control and Computing, IC-C 2024

ISBN: (纸本)9798350351873

The proceedings contain 14 papers. The topics discussed include: a study on optimizing indoor air quality through the integration of multi-point monitoring and the design of a wet scrubber;phytoplankton classification using a simplified convolutional neural network and edge enhancement;threat reduction framework for intelligent systems;advancing autonomous mobile robots with the dynamic window approach: an exploration of integrated simulation for enhanced performance;a two-stage adaptive improved genetic algorithm for flexible job-shop scheduling problem with AGVs;and machine learning-based intent analysis system using voice recognition for imported food information delivery.

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Zeroth-Order Feedback Optimization for Inverter-based Volt-VAR control in Wind Farm 9

Zeroth-Order Feedback Optimization for Inverter-based Volt-V...

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9th International conference on Power and Renewable Energy

作者： Li, Siyun Wu, Wenchuan Xu, Jun Dong, Jihui Wang, Fanhua Yuan, Qihai Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Sichuan Energy Internet Res Inst Chengdu Peoples R China Inner Mongolia Chahar New Energy Co Ltd China Ulanqab Peoples R China

ISBN: (纸本)9798350377477;9798350377460

Significant penetration of wind power into power systems gives rise to severe power quality concerns, especially voltage violations. In wind farms, the system model parameters are usually inaccurate or even unknown, which brings great difficulties to the implementation of conventional model-based control methods. Moreover, wind farms are usually partially observable since only a few measurements are available at critical nodes. To provide a practical Volt/Var control (VVC) strategy for such networks, a novel model-free VVC method based on zeroth-order feedback optimization is proposed in this paper, which doesn't rely on any system model parameters. Firstly, the proposed method continuously collects system output measurements and evaluates the objective function value at the current input and at the measured output, thus constructing gradient estimates. Then, the control inputs are updated via an accelerated gradient projection algorithm, where the step length is selected based on Barzilai-Borwein-like rules for constrained optimization to improve the speed of convergence. Numerical tests exhibit a promising performance of the proposed method in voltage control.

关键词： zeroth-order optimization reactive optimization model-free control renewable energy resources

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IoT-Enabled Real-Time Management of Smart Grids With Demand Response Aggregators

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第1期58卷 102-112页

作者： Estebsari, Abouzar Mazzarino, Pietro Rando Bottaccioli, Lorenzo Patti, Edoardo London South Bank Univ London SE1 0AA England Politecn Torino I-10129 Turin Italy

Integration of widely distributed small-scale renewable energy sources like rooftop photovoltaic panels and emerging loads like plug-in electric vehicles would cause more volatility in total net demand of distribution networks. Utility-owned storage units and control devices like tap changers and capacitors may not be sufficient to manage the system in real-time. Exploitation of available flexibility in demand side through aggregators is a new measure that distribution system operators are interested in. In this article, we present a developed real-time management schema based on internet of things solutions which facilitate interactions between system operators and aggregators for ancillary services like power balance at primary substation or voltage regulation at secondary substations. Two algorithms for power balance and voltage regulation are developed based on modified optimal power flow and voltage sensitivity matrix, respectively. To demonstrate the applicability of the schema, we set-up a real-time simulation-based test bed and realized the performance of this approach in a real-like environment using real data of a network with residential buildings.

关键词： Real-time systems Smart grids Substations Renewable energy sources Costs Voltage control Demand response Aggregator demand response internet of things (IoT) power flow real-time simulation

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Attack Detection on internet of Things Devices using Machine Learning Techniques 7

Attack Detection on Internet of Things Devices using Machine...

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7th International conference on Intelligent Computing and control systems, ICICCS 2023

作者： Sharma, Yatharth Kumar, Vansh Chaudhary, Himanshi KIET Group of Institutions Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350397253

Today, the exponential growth of technology and the boom in connection demand has resulted in significant growth of internet of Things (IoT) devices. In light of the enormous increase in smart devices, secure communication between them has become a major challenge due to an increase in cyberattacks. Adding hardware-based security is challenging due to the complex architecture of IoT devices and limited computation ability. Since IoT devices produce enormous amount of data, machine learning (ML) can be utilized to detect attacks on IoT devices. This study proposes some Machine Learning models to detect and prevent Botnet IoT attacks. The network-based Detection of Bait-and-Switch IoT Attacks (N-BaIoT) dataset is used to train the models. The performance of models is analyzed using precision, recall and F1-score by carrying out multiclass classification. Experimental result shows that some of the proposed models are efficient in detecting botnet attacks with 99% accuracy. The model can also extend to use complete dataset and new ML techniques. © 2023 IEEE.

关键词： internet of things

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