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2023 2nd International conference on the Energy internet and Energy Interactive Technology, EIEIT 2023

2023 2nd International Conference on the Energy Internet and...

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2023 2nd International conference on the Energy internet and Energy Interactive Technology, EIEIT 2023

The proceedings contain 17 papers. The topics discussed include: a robust parameter identification approach with anti-outlier characteristics for lithium-ion batteries;compressor operation monitoring and optimization method for large-scale natural gas pipelines;effect of fuel injection advance angle on the performance of ethylene glycol/diesel engine;adaptive coordination control based on alkaline water electrolyzers and battery driven by wind power;application of uncertainty analysis in distributed planning of urban comprehensive energy system;reactive power compensation optimization of distribution network with distributed power supply;studying interoperability of different compensation networks in inductive power transfer systems;a novel fault current full compensation method in active flexible grounding distribution networks;and low-frequency defense strategy for wind turbine participating in power grid frequency adjustment.

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Identification of Important Features at Different IoT layers for Dynamic Attack Detection 9

Identification of Important Features at Different IoT layers...

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9th IEEE International conference on Big Data Security on Cloud, 9th IEEE International conference on High performance and Smart Computing and 9th IEEE International conference on Intelligent Data and Security, BigDataSecurity-HPSC-IDS 2023

作者： Haque, Safwana El-Moussa, Fadi Komninos, Nikos Muttukrishnan, Rajarajan City University of London London United Kingdom Bt Group Plc Ipswich United Kingdom

ISBN: (纸本)9798350312935

With the growth of internet of Things (IoT), it is essential to devise mitigation techniques to curb related attacks to prevent losses and network disruptions. One popular method of achieving this is with the use of intrusion detection systems (IDSs) that incorporate machine learning (ML) techniques to automatically detect attacks and anomalies. The main purpose of this study was to compare attacks within the same layer (e.g. network layer) or across two different layers (e.g. network and application layers) in order to determine features that are common or unique to each attack and to further determine if attacks belonging to different layers have distinguishing features. To achieve this outcome, the Edge-iioT dataset was taken into consideration, a recent IoT-related dataset with a varied collection of attack signatures collected using IoT and iioT (Industrial IoT) sensors. This study mainly focuses on home IoT networks and therefore network traffic flows primarily from five sensors that can be commonly found in a home IoT network were selected from the Edge-iioT dataset for the ML processes. For the scope of this study, transmission control protocol (TCP) and user datagram protocol (UDP) distributed denial of service (DDoS) attacks were selected as the network layer attacks, while structured query language (SQL) injection and cross-site scripting (XSS) attacks were selected as the application layer attacks. The study identified three features as unique to application layer attacks, four to the XSS attack only, and three to the UDP DDoS attack only. This study also showed that the Edge-iioT dataset can be reduced to 20 features for optimal ML results (Naïve Bayes [NB] improved to 89.2%, J48 to 94.7% and k-nearest neighbour [k-NN] to 91.7%) using the gain ratio feature selection technique. © 2023 IEEE.

关键词： internet of things

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Reliable Multi-Link Framework for Command and control Communication in Remotely Piloted Aircraft systems

Reliable Multi-Link Framework for Command and Control Commun...

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2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Sridharan, Shreeja Von Mankowski, Jörg Wöllert, Thomas Werner, Markus Kellerer, Wolfgang Technical University of Munich Communication Networks Munich80333 Germany Atmosphere GmbH Wessling82234 Germany

ISBN: (纸本)9798350351255

Unmanned Aerial Vehicles (UAVs) have created new opportunities for various applications in civil airspace. One critical aspect of a successful operation is to establish reliable communication between a UAV and the remote pilot in a Ground control Station in Remotely Piloted Aircraft systems. The effectiveness of this communication, known as C2 communication, depends on the underlying network technology (LTE/5G, Satellite, etc.) and the specific application telemetry protocol. A single C2 link using cellular or satellite communication technology fails to satisfy the ICAO C2 performance requirements [1]. Therefore, a multi-link framework for C2 communication is widely adopted. However, the homogeneous cellular-based multi-link [2] experimental solutions have limitations concerning the geographical location of Beyond Visual Line-of-Sight (BVLOS) operations. Additionally, simulation analysis of the heterogeneous multi-link [3] solution, using cellular and GEO-satellite communication, is inadequate to conclude the link performance requirements (bandwidth and latency) for the telemetry protocol. Therefore, we propose a heterogeneous 5G and LEO-satellite Iridium multi-link network architecture for C2 communication. Our quantitative analysis of the link technology present in this architecture demonstrates (i) the required logical Link Quality Indicator for C2 communication, and (ii) the possible solutions for C2 telemetry protocol to utilize the link capabilities in this architecture. We demonstrate that a cellular link can provide a throughput of 3.5, 8.5 Mbps and 30 ms latency for 5G, whereas an Iridium satellite link can provide 22, 88 kbps (uplink, downlink) throughput and 750 ms latency sufficient for reliable C2 communication. © 2024 IEEE.

关键词： Satellite communication systems

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On Minimizing TCP Traffic Congestion control in Vehicular internet of Things 3

On Minimizing TCP Traffic Congestion Control in Vehicular In...

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3rd International conference on Electronics and Sustainable Communication systems, ICESC 2022

作者： Jude M, Joseph Auxilius Diniesh, V.C. Shanjeev, E.N. Saran, V. Tamilmani, B. Prathap Kumar, K. Kongu Engineering College Department of Ece Erode India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Vehicular communication is an emerging field and will experience a substantial rise in wireless exchange of information. Heavy usage of internet traffic introduces a higher likelihood of network congestion. Several internet congestion control algorithms are used to regulate transmission rates that continuously monitor packet losses and timeouts to identify congestion in the network. In the vehicular environment, packet loss eventuality happens due to mobility and channel losses. In this paper, a new algorithm called the Congestion Aware Rate Tuning (CART) is proposed to identify network congestion based on the estimation of Bottleneck Limit Threshold (BLT) parameter. This algorithm won't respond to packet loss and timeout conditions. The parameters average throughput, average goodput and mean delay are taken as metrics to analyse the performance of this algorithm. Simulation results of the comparison between the existing Transmission control Protocol Westwood algorithm and CART algorithm in NS-3 shows a considerable increment in throughput, goodput, and mean delay in vehicular conditions. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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Towards an intelligent system to manage IDS for IoT 5

Towards an intelligent system to manage IDS for IoT

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5th conference on Cloud and internet of Things (CIoT)

作者： Khoulimi, Hind Lahby, Mohamed Benammar, Othman Higher Normal Sch Appl Math & Comp Lab Casablanca Morocco

ISBN: (纸本)9781665479646

Nowadays, the security of information system has become more and more important in our lives. Indeed, the appearance of 5G see 6G and technological progress which has given rise to the democratization of connected objects, thus increasing the related risks and making the task of information system security administrator more and harder. To remedy this, the researchers focused on several systems including IDS which is an Intrusion Detection System used in host and network security. However, this system generates a large number of alarms which must be managed by a security administrator, something which is not easy to do, but is necessary to guarantee an optimal level of security. In this work, we will present a system that helps the security administrator to properly detect and manage IDS alerts. This system is based on detecting attacks, collecting alerts generated by different IDS in a network of objects, analyzing these alerts and taking appropriate actions. We propose automation of said tasks based on artificial intelligence algorithms, especially Deep Learning. Our choice is directed towards the algorithm of the Artificial Neural network (ANN) according to several criteria namely the performance and the speed of detection which is our major concern while combining it with the algorithm of Spider Monkey Optimization (SMO) for a good optimization of the entries. Our system aims to strengthen the second line of defense and make it more efficient and intelligent by equipping it with three intelligent engines namely, a detection engine, an analysis engine and an action engine. To illustrate the applicability of the proposed approaches, we begun to test the performance of detection by using different measures for example error of detection, training time and accuracy rate which have been obtained by testing with NSL-KDD dataset.

关键词： Intrusion Detection System (IDS) internet of Things (IoT) Computer Security Artificial Intelligence (AI) Deep Learning (DL) Artificial Neural network (ANN) Spider Monkey Optimization (SMO) Alerts Actions

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MTRNet: An Efficient Hybrid network Model for Surface Defect Detection of Sheet Metal 36

MTRNet: An Efficient Hybrid Network Model for Surface Defect...

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36th International conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Hao, Huijuan Zhu, Sijian Chen, Yu Yi, Changle Zhao, Hongge Feng, Yue Yang, Rong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Addressing the challenges posed by the insufficient computational power of low-spec devices to achieve the inference efficiency of prevailing deep learning models, alongside the variability in type and shape within industrial metal sheet datasets, while also catering to the demand for high-precision detection, this paper proposes a novel single-stage detection architecture termed MTRNet. In this paper, we introduce three key enhancements: Firstly, to reduce the number of parameters, we propose a novel Partial Depth Convolution (PDC) structure. By minimizing redundant computations, we aim to enhance the efficiency of spatial feature extraction. Secondly, we propose a Convolution Transformer (CTR) structure that replaces the self-attention module with a convolutional module. This modification addresses the computational inefficiency inherent in the self-attention mechanism of existing Visual Transformers (ViT). Finally, we introduce a novel Attention Convolution Transformer (ACTR) structure to enhance the extraction of global and local feature information. This architecture seamlessly integrates the strengths of both attention and convolution mechanisms, working synergistically to improve performance. Our proposed MTRNet network demonstrates superior detection performance compared to existing industrial standards, achieving a detection accuracy of 81.5% on the NEU-DET dataset. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Deep learning

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An Improved Support Vector Machine Attack Detection Algorithm for Industry controls System 3

An Improved Support Vector Machine Attack Detection Algorith...

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3rd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Cui, Shangqing Feng, Dongqin Zhejiang University College of Control Science and Engineering Hangzhou China

ISBN: (纸本)9781665472784

With the application of internet technologies in the industrial field, industrial control systems (ICS) have the possibility of suffering malicious network attacks. Because ICS is widely used in critical infrastructure, designing attack detection algorithms for it can effectively reduce losses. Many papers have built physical models for cyber-attacks against ICS and the corresponding detection algorithms have been proposed. However, the detection performance can be further improved. This paper establishes the simulation attack on the Tennessee Eastman (TE) process and proposes an improved support vector machine (SVM) method to detect integrity attacks. This algorithm uses contribution plots and recursive feature elimination to select features. Compared with the principal component analysis (PCA) feature extraction, the detection accuracy is improved. After adding system dynamics to the algorithm, the time to detect attacks is advanced. © 2023 IEEE.

关键词： Support vector machines

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performance Analysis of network Coverage Area in WSN Using Optimization Techniques 2nd

Performance Analysis of Network Coverage Area in WSN Using O...

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2nd International conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Prabhu, R. Nagarajan, R. Kalis, K.R.N. Kannadhasan, S. Department of Electronics and Communication Engineering Gnanamani College of Technology Tamil Nadu Namakkal India Department of Electrical and Electronics Engineering Gnanamani College of Technology Tamil Nadu Namakkal India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamil Nadu Trichy India Department of Electronics and Communication Engineering Study World College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789819995233

Wireless sensor networks (WSNs) are created by deploying various sensor units in harsh environments for target monitoring, as well as by deploying various tiny sensors collectively. The sensor units monitor the data in their vicinity and send it to the sink for processing. However, due to various parameters such as coverage area, in-built sensing and processing units, and so on, the sensor nodes may consume more power when forwarding information to the sink. We concentrate on the existing issues in the coverage control of various sensor models for quantitatively and qualitatively solving the sensor network coverage area. Various optimization techniques such as spotted hyena optimization (SHO), particle swarm optimization (PSO), and moth flame optimization (MFO) are studied and discussed. The trade-off between coverage rate, total number of sensor nodes, and energy consumption was observed, and the techniques’ significance was compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of things

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Study and Analysis of Various Crop Prediction Techniques in IoT network: An Overview

Study and Analysis of Various Crop Prediction Techniques in ...

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2022 International conference on Automatic control and Intelligent systems

作者： Rajathi, S. Anitha Sahayadhas, Arun Melvin, Allan Vels Inst Sci Technol & Adv Studies VISTAS Sch Engn Dept CSE Chennai Tamil Nadu India Univ Malaysia Perlis Fac Elect Engn Technol Pauh Putra Campus Arau Perlis Malaysia

ISBN: (纸本)9781665495813

In the agriculture domain, the main issues determined are knowledge deficiencies regarding alterations in climate. As each crop poses its own climatic features, but the issues confronted in agriculture are managed using precise farming methods. The precision farming assists in fulfilling the demand for food, maintaining crop productivity, and increasing the yield rate. In India, the emerging needs require sustainable agriculture. The two main issues in agriculture are the selection of crops and altering climatic conditions, which are solved by examining prediction and monitoring techniques, but there is no solution for the crop suggestion. This survey examines several techniques based on crop prediction in the IoT network. This study utilizes 25 research papers focused on several methods, and review of researches based on the classical technique is devised. The assessment is done based on publication year, research technique, implementation tools, performance measures and achievement of the research methodologies towards crop prediction techniques in IoT network. At the end, the research gaps and issues of the existing techniques are devised in such a way that the motivation for developing an effective method for crop prediction techniques in IoT network is revealed.

关键词： Crop prediction internet of Things Smart agriculture Production IoT sensors

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Fine-Grained Flow control Agent on Path MTU for IoT Software 23

Fine-Grained Flow Control Agent on Path MTU for IoT Software

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14th Asia-Pacific Symposium on internetware, internetware 2023

作者： He, Hongchuan Chen, Dezhi Zhuang, Zirui Qi, Qi Zhang, Lejian Xu, Tong Wang, Jingyu Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798400708947

internet of Things (IoT) software is used to control the distributed hardware of the underlying network and provide a reliable operating platform for various services. In production system, diversity IoT software provides multiple services, flows of different software run in parallel on the same IoT platform. Thus, system-level network parameter configurations may not be suitable for all service needs. In this paper, we focus on the challenge of the personally parameterizing transmission unit size and congestion windows (CWND) in flow control. We propose deeper flow control software model (DeepFC) for finer-grained flow control than traditional algorithms. DeepFC consist of two parts: (i) Since system-level transmission unit size may degrade network performance due to frequent fragmentation, we combine path MTU (PMTU) and deep reinforcement learning (DRL) to predict fine-grained flow-level transmission unit size. (ii) Transmission unit size is related to CWND in flow control. The fine-grained transmission unit size needs fine-grained congestion control solution. In DeepFC, we consider the mutual coupling between transmission unit size and CWND parameter configuration to further improve network performance. Experimental results show that DeepFC can reduce fragmentation by 67.8% compared to the protocols with system-level transmission unit size, flow completion time can be reduced by 20.93%, and throughput can be increased by 19.96% compared to the average of benchmark algorithms. © 2023 ACM.

关键词： Reinforcement learning

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