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IoV-BERT-IDS: Hybrid network Intrusion Detection System in IoV Using Large Language Models

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 1909-1921页

作者： Fu, Mengyi Wang, Pan Liu, Minyao Zhang, Ze Zhou, Xiaokang Nanjing Univ Post & Telecommun Sch Modern Posts Nanjing 210003 Peoples R China Shiga Univ Fac Data Sci Hikone 5228522 Japan RIKEN RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Kansai Univ Fac Business Data Sci Osaka 5650823 Japan

The traditional vehicular ad hoc network (VANET) gradually evolved into the internet of Vehicles (IoV), which has also become a potential target for attacks and faces security challenges in an open network environment. Intrusion detection systems (IDS) based on machine learning (ML) and deep learning (DL) are introduced to mitigate security threats. However, existing ML/DL-based IDS suffer from challenges in IoV environments. First, due to the limitations of ML/DL-based methods, classification performance is unsatisfactory when they extract only unidirectional contextual features or spatial characteristics. Second, existing research on in-vehicle network IDS often limits validation and testing to a static dataset of a single vehicle model. This approach may not adequately address diverse potential attacks in a dynamic environment. Third, few studies of hybrid IDS can simultaneously implement in-vehicle and extra-vehicle network intrusion detection. Large language models (LLM) have shown outstanding applications in fields such as natural language processing (NLP) and computer vision (CV). In particular, bidirectional encoder representations from transformers (BERT) obtain new state-of-the-art results on eleven famous NLP tasks. Consequently, this paper introduces a hybrid network IDS in IoV utilising LLM, denoted as IoV-BERT-IDS. This framework encompasses four modules: semantic extractor (SE), input embedding, IoV-BERT-IDS pre-training, and IoV-BERT-IDS fine-tuning. To conform to the BERT model, the semantic extractor is introduced to transform traffic data devoid of apparent semantics into contextual semantics, comprising bidirectional and unidirectional SE. Through SE, controller area network (CAN) data is transformed into a CAN byte sentence (CBS), while extra-vehicle network traffic data is transformed into a traffic byte sentence (TBS). Additionally, two pre-training tasks, the masked byte word model (MBWM) and next byte sentence prediction (NBSP) are proposed

关键词： Semantics Encoding Bidirectional control Task analysis Natural language processing Computational modeling Telecommunication traffic BERT intrusion detection system internet of Vehicle large language model pre-training model

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Worker Activity Recognition in Manufacturing Line Using Near-Body Electric Field

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IEEE internet OF THINGS JOURNAL 2024年第7期11卷 11554-11565页

作者： Suh, Sungho Rey, Vitor Fortes Bian, Sizhen Huang, Yu-Chi Rozanec, Joze M. Ghinani, Hooman Tavakoli Zhou, Bo Lukowicz, Paul German Res Ctr Artificial Intelligence DFKI Dept Embedded Intelligence D-67663 Kaiserslautern Germany RPTU Kaiserslautern Landau Dept Comp Sci D-67663 Kaiserslautern Germany Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland Jozef Stefan Int Postgrad Sch Informat & Commun Technol Ljubljana 1000 Slovenia German Res Ctr Artificial Intelligence DFKI Dept Innovat Factory Syst D-67663 Kaiserslautern Germany

Manufacturing industries strive to improve production efficiency and product quality by deploying advanced sensing and control systems. Wearable sensors are emerging as a promising solution for achieving this goal, as they can provide continuous and unobtrusive monitoring of workers' activities in the manufacturing line. This article presents a novel wearable sensing prototype that combines IMU and body capacitance sensing modules to recognize worker activities in the manufacturing line. To handle these multimodal sensor data, we propose and compare early, and late sensor data fusion approaches for multichannel time-series convolutional neural networks and deep convolutional LSTM. We evaluate the proposed hardware and neural network model by collecting and annotating sensor data using the proposed sensing prototype and Apple Watches in the testbed of the manufacturing line. Experimental results demonstrate that our proposed methods achieve superior performance compared to the baseline methods, indicating the potential of the proposed approach for real-world applications in manufacturing industries. Furthermore, the proposed sensing prototype with a body capacitive sensor (BCS) and feature fusion method improves by 6.35%, yielding a 9.38% higher macro F1 score than the proposed sensing prototype without a BCS and Apple Watch data, respectively.

关键词： Sensors Human activity recognition Manufacturing Capacitance Robot sensing systems internet of Things Hidden Markov models Human activity recognition (HAR) multimodal fusion multimodal sensors smart manufacturing wearable sensors

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Model Predictive Power control Strategy for Multiport Magnetic network Energy Routers 6

Model Predictive Power Control Strategy for Multiport Magnet...

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6th International conference on Circuits and systems

作者： Zhao, Yi Deng, Fujin Yin, Zheng Abulanwar, Sayed Southeast Univ Sch Elect Engn Nanjing Peoples R China Mansoura Univ Fac Engn Elect Engn Dept Mansoura 35516 Egypt

ISBN: (纸本)9798350352153;9798350352160

Magnetic network energy router (MNER) has the advantages of high transmission efficiency and high-power quality in distributed energy consumption. Traditional proportional-integral (PI) control applied to MNER converters exhibits slow dynamic response and insufficient multi-objective control capability. To solve this problem, a model predictive power control (MPPC) strategy is proposed in this paper for MNER converter, which can achieve the free flow of power between different ports, has better dynamic performance than PI control. Simulation results demonstrate the effectiveness of the proposed MPPC strategy.

关键词： Magnetic network energy router (MNER) model predictive power control (MPPC) active power transmission

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Research and Analysis of Electric control systems Based on Fuzzy Logic and Neural network Integration 4

Research and Analysis of Electric Control Systems Based on F...

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4th Asia-Pacific conference on Communications Technology and Computer Science, ACCTCS 2024

作者： Yang, Xuejian Xu, Yuanfei Zhang, Lei Yunnan Tobacca Corporation Dali Prefecture Yunnan Dali China China Tobacca Corporation Yunnan Company Yunnan Kunming China

ISBN: (纸本)9798350359985

With the rapid development of electrical engineering, electric control systems have entered a period of high-quality development. By focusing on fuzzy logic-based neural networks, new proposals should be submitted to meet the needs of intelligent and adaptive electric control systems. Based on the dynamic evolution of electrical control systems, we establish a theoretical analysis framework for modeling electric control systems according to fuzzy logic. It allows us to explain the integrated system state identification mechanism and performance optimization of electric control systems, including fuzzy logic and neural networks. In addition, we use numerical examples and practical deduction to explore the possibility of moving toward the high-quality development of electric control systems. The development of an electric control system is to provide electrical equipment with functions that meet expected standards. Managers are committed to continuously improving electric control systems' quality and equipment satisfaction. To this end, we have taken measures, including strengthening fuzzy logic control based on fuzzy rules and internal circulation, constructing a mechanism for interaction and learning between fuzzy logic and neural network quality perception, and establishing an evaluation system for electric control system and electrical equipment, to achieve high-quality development of electric control system, boost technological upgrading, and truly meet user needs. © 2024 IEEE.

关键词： Adaptive control systems

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Industrial internet of Things Intrusion Detection Model Integrating Graph Attention network and Gated Temporal Convolutional network 7

Industrial Internet of Things Intrusion Detection Model Inte...

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7th International conference on Advanced Algorithms and control Engineering, ICAACE 2024

作者： Peng, Chenghe Zhang, Ying College Of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798350361445

In recent years, the application of the Industrial internet of Things (IIoT) in industry has become increasingly important. However, because IIoT devices and systems are connected to the internet, they may face the risk of malicious intrusions that can cause serious damage to production processes and data security. Intrusion detection systems (IDS) can be used to monitor IIoT for malicious activities and develop response strategies. Therefore, this article proposes an intrusion detection model called GTRGAT to ensure the network security of IIoT. In particular, the GTRGAT model uses GTCN to quickly pull out sequence features from IIoT traffic. GTCN combines convolutional operations and gating mechanisms, aiming to process time series data more efficiently and capture its intrinsic patterns. By using the TON-IoT dataset, the performance of the GTRGAT model is verified, showing that it can accurately detect intrusions. The experiment also showed that the GTRGAT model works better than other advanced methods, so it can be used as a workable way to find intrusions in industrial IoT networks. © 2024 IEEE.

关键词： internet of things

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Optimized Jordan Neural network and Bandwidth Aware Routing Protocol for Congestion Prediction and Avoidance in IOT for Effective Communication

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OPTICAL MEMORY AND NEURAL networkS 2024年第4期33卷 429-446页

作者： Suma, Mallavalli Raghavendra Shankarrao, Bhosale Rajkumar Gopi, Adapa Sambhe, Nilesh U. Umate, Laxmikant Dayananda Sagar Coll Engn Bengaluru 560078 India Amrutvahini Coll Engn Sangamner 422608 Maharashtra India Koneru Lakshmaiah Educ Fdn Guntur 522302 Andhra Pradesh India Yeshwantrao Chavan Coll Engn Nagpur 441110 India Datta Meghe Inst Higher Educ & Res Sawangi Meghe Wardha 442107 India

Development of 5G internet in today's trend leads to the evaluation of many IOT devices. The information is transmitted by a network in IOT to store the data in the cloud. Due to the wide usage of IOT devices by people, congestion may occurs in IOT networks, which delays the information or sometimes resulting in data loss despite the implementation of congestion control methods. So many machine learning and congestion control protocols are used to predict and avoid congestion in IOT network. But these existing systems consist of drawbacks such as accuracy drop for prediction, packet loss and time delay. Hence, the Bandwidth Aware Routing Strategy (BARS) protocol using Jordan Neural network (JNN) was developed to predict and avoid congestion in the network. Initially, the IOT nodes are deployed and the data are collected and preprocessed using a sigmoidal function and Extreme Learning machine to improve the quality of the original data. Then extract the features from the pre-processed data using Locality Preserving Projection (LPP). After that, Jordan Neural network is used for congestion prediction and pine cone optimization is used to tune the hyper parameters such as learning rate and batch size which is utilized to improve the classifier performance. Then, BARS protocol is used to avoid the congestion present in the IOT network. According to the experimental approach, the proposed techniques achieves 95.45% of Accuracy, 95.71% of Precision, 95.39% of F1-Scorce and 95.02 of specificity. Thus, the congestion and avoidance of Information in the IOT network is processed in high efficiency by using this proposed approach.

关键词： congestion control sigmoidal function Extreme Learning Machine congestion avoidance Jordan Neural network pine cone optimization BARS

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SoftFarmNet: Reconfigurable Wi-Fi HaLow networks for Precision Agriculture 12

SoftFarmNet: Reconfigurable Wi-Fi HaLow Networks for Precisi...

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12th International conference on Cloud networking (CloudNet)

作者： Ahmed, Nurzaman Esposito, Flavio Okafor, Okwudilichukwu Shakoor, Nadia St Louis Univ Donald Danforth Plant Sci Ctr St Louis MO 63103 USA St Louis Univ Dept Comp Sci St Louis MO 63103 USA

ISBN: (纸本)9798350313062;9798350313079

networks deployed for internet of Agricultural Things (IoAT) applications are often deployed in remote areas with limited coverage and a lack of standardization across sensing devices, posing challenges to reliable connectivity and resilient data exchange. The IEEE 802.11ah standard, commonly known as Wi-Fi HaLow, offers the potential for wide coverage and support for a large number of IoAT devices. However, such protocol still faces efficiency suboptimalities in channel utilization, particularly when handling heterogeneous IoT applications with diverse quality of Service (QoS) requirements. To address these challenges, in this paper we propose SoftFarmNet, a reconfigurable IEEE 802.11ah (Wi-Fi HaLow) network management architecture, specifically designed for remote monitoring and control of agricultural-based IoAT. SoftFarmNet leverages a network traffic prediction-based slot scheduling and station grouping scheme to enhance channel utilization and support different QoS requirements of IoAT applications. The proposed architecture integrates Software-defined networking (SDN) at the edge, enabling configuration of low-level Wi-Fi HaLow parameters and the management of multiple network slices to dynamically meet application requirements. Our performance evaluation demonstrates substantial improvements in throughput, delay, and energy consumption, effectively optimizing channel usage, with respect to state-of-the-art solutions.

关键词： internet of Things internet of Agricultural Things IEEE 802.11ah Software-Defined networking quality of Service

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Energy-efficient routing protocol on public roads using real-time traffic information

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TELECOMMUNICATION systems 2023年第4期82卷 465-486页

作者： Ali, Zainab H. Ali, Hesham Arafat Kafrelsheikh Univ Fac Artificial Intelligence Dept Embedded Network Syst & Technol El Geish St Kafrelsheikh 33516 Egypt Mansoura Univ Delta Univ Sci & Technol Fac Engn Dept Comp Engn & Control Syst El Gomhoria St Mansoura Talkha 33511 Eldakahlia Egypt

Wireless technology has been significantly expanding to perform automotive services related to enhancing road networks and increasing traffic safety. internet of Vehicles (IoV), as a part of wireless sensor technology, has succeeded in making considerable progress in promoting and protecting human lives from the damage caused by traffic accidents. The road statistical reports demonstrated that utilizing data dissemination techniques in IoV to share timely information about the vehicle's position with drivers plays a key in avoiding accidents occurrence. This type of technique enables data propagation in a collaboratively and efficient manner to boost real-time performance. However, several limitations have arisen in network bandwidth usage and power consumption, as well as communication reliability with increasing packet loss probability. The main objective of this study is to present a new vehicular architecture that supports the integration of Software-Defined networks and fog computing for enhancing VANET performance. An energy-efficient and QoS-aware routing technique allowing for control of excessive dissemination of data traffic across the networks is also proposed. The simulation results show that the proposed routing protocol introduced an accurately controlling methodology in the transmission rate of data packets and communication reliability of VANET. There is a 50-60% enhancement of the whole power consumption and network throughput, depending on the implementation of the proposed geographical routing methodology.

关键词： Energy conservation Fog computing quality of service Routing methodology Software-defined-network Transmission reliability Vehicular ad hoc network

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Computer Communication network Delay and Traffic Distribution Based on Neural network Technology 6

Computer Communication Network Delay and Traffic Distributio...

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6th IEEE Information Technology, networking, Electronic and Automation control conference, ITNEC 2023

作者： Zhang, Yuling Lijiang Teachers College Lijiang China

ISBN: (纸本)9781665460033

With the rapid development of the current wireless communication and the internet, the number of mobile devices access is increasing day by day, people adapt to the mobile internet life, but also put forward higher requirements for the transmission quality and transmission rate of the wireless communication system. This paper aims to study the delay and traffic distribution of computer communication networks based on neural network technologies. This paper combines the network distribution technology and the load balancing technology to introduce the network traffic distribution mechanism in heterogeneous networks. A traffic allocation (NDBFA) algorithm based on network comprehensive delay is proposed. By analyzing the difference of the average delay variation in the heterogeneous network, correcting the shortcomings of the link delay traffic distribution algorithm, the simulation to verify that NDBFA can balance the business distribution in each network, especially in the heterogeneous network network services reach saturation throughput, the distribution algorithm has good distribution performance. A traffic allocation (LBFA) algorithm based on network load condition is proposed. This paper compares the packet delay performance and proves that the neural network prediction can effectively improve the network performance, reduce the delay of high priority services, and ensure the network quality of service. © 2023 IEEE.

关键词： Heterogeneous networks

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network Traffic Prediction and Congestion control Using a Feed Forward Neural network 12

Network Traffic Prediction and Congestion Control Using a Fe...

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12th IEEE International conference on Intelligent systems, IS 2024

作者： Nwigwe, JohnPaul Martin, Iain Ramsay, Craig University of Dundee United Kingdom

ISBN: (纸本)9798350350982

The surge in internet of Things (IoT) usage has precipitated the need to replace classical internet network prediction and congestion control methods with a more efficient and reliable learning-based approach. Many machine-learning based approaches have been offered by different studies to solve this problem but not without limitations. In this study, we propose a new feed forward neural network with a multi-channel routing congestion control system (FFNN-MCRCCS) to optimize network prediction and congestion control problem. The nodes in the proposed network are mutually connected to enable the firing of neurons in a more decentralized manner to update their weights in order to call the recursive square function to predict the traffic. The exponent value of the recursive function serves as a parameter to determine the channel with the minimum congestion threshold for packet transmission. The results of the experiment show that the proposed model has the capability to optimize network traffic for effective utilization of network resources more than the baseline and the counterpart methods. The performance evaluation metrics used for the experiment include mean square error throughput, link utilization, average jitter, settling time, queue length and network and network convergence. © 2024 IEEE.

关键词： Congestion control (communication)

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