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Anomaly Detection Method based on Semi-supervised Deep Learning for internet of Things Traffic 2

Anomaly Detection Method Based on Semi-supervised Deep Learn...

引用

2nd international conference on Machine Vision, image Processing and Imaging technology, MVIPIT 2024

作者： Liao, Niandong Li, Qilin Changsha University of Science and Technology 410000 China

ISBN: (纸本)9798331543037

Today, the internet of Things (IoT) is developing rapidly, with IoT traffic and attacks increasing sharply, posing a serious threat to the stability and security of IoT systems. In this condition, it is crucial to safeguard the normal operation of IoT systems, while traditional machine learning methods usually require manual selection of features with low anomaly detection rates. To tackle the issue of significant deviation in anomaly detection results caused by the imbalance of data samples in IoT traffic, an IoT traffic anomaly detection model (DCGAN-BiGRU) was proposed. Deep Convolutional Generative Adversarial Networks (DCGAN) were utilized to learn the inherent feature distribution of known attack sample data for the generation of new attack samples. Therefore, the disadvantage of unbalanced data samples was overcome and the training stability was improved. Bi-Gated Recurrent Unit (BiGRU) consists of two stacked GRU layers. Then the ability of the model to learn more complex patterns in the sequential data was enhanced. The CICIDS2018 and the CICIoT2023 datasets were used as experimental samples to evaluate the model. The DCGAN-BiGRU intrusion detection model enhanced the efficiency and precision of traffic anomaly detection when compared to traditional methods. ©2024 IEEE.

关键词： Generative adversarial networks

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A Secure and Distributed Decision based Handover Scheme for Low Earth Orbit Satellites in 6G internet 21

A Secure and Distributed Decision Based Handover Scheme for ...

引用

21st international Bhurban conference on Applied Sciences and technology, IBCAST 2024

作者： Arshad, Muhammad Liu, Jianwei Usman, Muhammad Khalid, Waqar School of Cyber Science and Technology Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (纸本)9798331516680

The space internet offers wireless communication services at any location and anytime. Therefore, satellite communication plays a vital role in the 6G internet. However, the rapid movement of LEO satellites leads to quick transfers between ground stations. Previous research has focused on increasing authentication security ahead of handover while minimizing signalling and computation complexity. The rapid movement of satellites could lead to signaling problems and potential security risks at handover operation. Efficient and secure LEO satellite handover operation is essential to achieve broad coverage in 6G internet. This study proposes a secure and decentralized LEO satellite handover mechanism with ground stations. In the approach, LEO satellites make predictable decisions based on predictable trajectory, signal strength, and link reliability. Following predicted decisions, blockchain applications are utilized to accomplished a secure handover mechanism. The generated simulation outcomes demonstrate that the proposed scheme effectively manages the handover of LEO satellites to a different ground station in satellite communication. The performance analysis demonstrates less computing cost and signal overhead in handover operation. © 2024 IEEE.

关键词： Satellite communication systems

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SEP: Stage-Enhanced Panoptic Segmentation based on Fully Convolutional Networks 15

SEP: Stage-Enhanced Panoptic Segmentation based on Fully Con...

引用

15th international conference on signal Processing systems, ICSPS 2023

作者： Ji, Shucheng Yuan, Xiaochen Bao, Junqi Faculty of Applied Science Macao Polytechnic University 999078 China

ISBN: (纸本)9781510675056

Panoptic segmentation is a critical technology in the field of multimedia, applicable to various domains such as autonomous driving and image recognition. However, due to the enormity and complexity of the task, enhancing the efficiency and accuracy of panoptic segmentation remains a challenge. In this paper, we propose a stage-enhanced panoptic segmentation method which improves the feature extraction network of the backbone, incorporates a stage feature fusion network, and designs a module for adaptive stage feature weight allocation. These enhancements optimize the overall network and enrich the stage features. Experimental results on the publicly available COCO-2017 dataset confirm the performance of Stage-Enhanced Panoptic and demonstrate its superiority compared to other methods. © 2024 SPIE.

关键词： image segmentation

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An efficient network for bacterial colony counting based on Tail-MLP

An efficient network for bacterial colony counting based on ...

引用

2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Li, Meng Xu, Xinan Lu, Yu College of Big Data and Internet Shenzhen Technology University Guangdong ShenZhen518000 China

ISBN: (纸本)9781510680425

This study introduces an innovative approach to microbial colony counting, leveraging the capabilities of convolutional neural networks (CNNs) and a novel Tail-MLP (Multilayer Perceptron) *** methods of colony counting are labor-intensive and prone to subjective errors, underscoring the need for *** advancements in deep learning offer a promising solution to these *** work presents a CNN-based model that incorporates the Tail-MLP architecture, specifically designed to enhance the accuracy and efficiency of colony enumeration across various sample *** a series of comparative experiments, we demonstrate the superior performance of our approach over existing methods, highlighting its fast counting speeds, high accuracy, ease of use, and excellent generalization ***, we address key challenges in current counting practices, providing insights into the potential for technological improvements in microbiological *** contributions include a critical analysis of existing colony counting methodologies, the design and implementation of a deep learning-based model for automated colony counting, and the validation of our Tail-MLP architecture, which significantly outperforms traditional deep learning models in both accuracy and processing *** work not only advances the field of microbial colony counting but also offers a methodological framework for applying deep learning techniques to complex biological image processing tasks. © 2024 SPIE.

关键词： Convolutional neural networks

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Method and Implementation of Intelligent Embedded systems based on Deep Learning technology 6

Method and Implementation of Intelligent Embedded Systems Ba...

引用

6th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Jing, Chuanli Yu, Chenggong School of Computer Science and Technology Qilu University of Technology Shandong Jinan250353 China

ISBN: (纸本)9781837242672

With the rapid development of the internet of Things and artificial intelligence technologies, intelligent embedded systems face increasingly complex challenges and demands. This paper proposes an intelligent embedded system approach based on deep learning technology and analyzes its implementation. First, the concept and characteristics of intelligent embedded systems, as well as the development and application of deep learning technology, are introduced. Next, the basic principles of deep neural networks and design optimization strategies for deep learning processors are described. Then, based on the analysis of application scenarios and cases of deep learning in embedded systems, an embedded system model based on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Reinforcement Learning (RL), and Generative Adversarial Networks (GAN) have been developed. This model is applied to image processing, speech recognition, robot control, and data augmentation, and verified in real-world conditions. Finally, the paper summarizes the advantages and development trends of intelligent embedded systems based on deep learning technology and discusses their potential and challenges in the future. Furthermore, the paper highlights the importance of optimizing hardware-software integration to achieve enhanced performance, energy efficiency, and real-time processing in intelligent embedded systems. © The Institution of Engineering & technology 2024.

关键词： Adversarial machine learning

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Proceedings - 2024 international conference on Interactive Intelligent systems and Techniques, IIST 2024

Proceedings - 2024 International Conference on Interactive I...

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2024 international conference on Interactive Intelligent systems and Techniques, IIST 2024

ISBN: (纸本)9798350374421

The proceedings contain 134 papers. The topics discussed include: using GPUs and neuromorphic computing for modern challenges in computing architecture;modeling and personalized recommendation of interest for internet users using LDA;computer vision-driven multi-target recognition and dynamic tracking;application of improved association rule algorithm in higher education teaching management;emotion analysis of instant music video live using BERT model: emotional polarity detection and emotional feedback;research on the application of interactive intelligent technology for action judgment in martial arts training;eigenvalue extraction of automated pothole road image based on AlexNet model;and construction of art virtual simulation laboratory resource sharing system based on machine learning algorithm.

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A Multi-Model Fusion Framework to Enhance sEMG-based Gesture Recognition Assisted by Visual Features

A Multi-Model Fusion Framework to Enhance sEMG-Based Gesture...

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2024 IEEE international conference on Robotics and Biomimetics, ROBIO 2024

作者： Gong, Longxiao Cheng, Yue Zhang, Hao Wang, Yang Zhao, Ping School of Mechanical Engineering Hefei University of Technology Anhui Hefei230009 China University of Science and Technology of China Department of Automation Anhui Hefei230026 China Hefei University of Technology Department of Mechanical Engineering Anhui Hefei230009 China

ISBN: (纸本)9781665481090

Gesture recognition plays an increasingly important role in fields such as human-computer inter-action and rehabilitation robots and therapy. However, most current gesture recognition systems are based on a single type of sensor, which limits the feature sources for gesture recognition. Therefore, this paper proposes a time-frequency and visual feature fusion multimodal recognition framework (TFVF -CNN). It combines the deep features extracted from a small amount of image data with the time-frequency fea-tures of surface electromyogram (sEMG) signals to improve the accuracy of gesture recognition based on sEMG. Additionally, this paper introduces a multi-modal dataset (MEVD) containing raw sEMG signal data, sEMG signal time-frequency data, and image data, providing a reliable data source for experiments. Finally, the experimental results show that gesture recognition accuracy with multimodal fusion features and time-frequency features based on sEMG signals is 93.88 % and 95.9 %, respectively. Furthermore, we verified the performance of gesture recognition under partial image occlusion and found that TFVF -CNN still exhibits superior performance. © 2024 IEEE.

关键词： Electromyography

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Enhancing CT image resolution with a hybrid transformer-based generative adversarial network 4

Enhancing CT image resolution with a hybrid transformer-base...

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4th international conference on Green Communication, Network, and internet of Things, CNIoT 2024

作者： Li, Yunhe Yang, Mei Wu, Haitao Bian, Tao Zhaoqing University Zhaoqing526060 China Shenzhen Chenzhuo Technology Company Shenzhen518055 China

ISBN: (纸本)9781510685710

Computed tomography (CT) imaging systems often produce images with lower resolution than ideal, particularly when using low-dose protocols to minimize radiation exposure. To address this limitation, we propose CTMTGAN, a novel super-resolution analysis method for enhancing CT image resolution using Generative Adversarial Networks (GANs). CTMTGAN integrates VMamba's TDSS module with Transformer-based attention blocks in its generator, trained with a PatchGAN discriminator and VGG-19 perceptual feature extractor. Experimental results demonstrate that CTMTGAN outperforms state-of-the-art methods in no-reference image quality metrics, producing clearer details, sharper edges, and fewer artifacts. Its ability to generate high-quality, high-resolution CT images suggests potential to improve diagnostic accuracy in clinical settings. © 2024 SPIE.

关键词： Computerized tomography

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An infrared gas detection algorithm based on weak supervision

An infrared gas detection algorithm based on weak supervisio...

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2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Xie, Lipeng Zhang, Chi Yang, Cong Li, Xuexiang School of Cyberspace Security Zhengzhou Univ No.97 Wenhua Road Zhengzhou Zhengzhou City China

ISBN: (纸本)9781510680425

Infrared image recognition technology has a wide range of applications in the field of gas detection. Unlike visible light images, gas detection in infrared images is relatively difficult due to the lack of clear contrast and the relative blurriness of gas targets. This paper proposes a weakly supervised distillation network to address the issue of low detection accuracy of gas regions in infrared images in complex scenes. This method mainly generates accurate heatmaps as pseudo labels by utilizing complex class activation mappings;Using pseudo labels to train the specialized model proposed in this article, more accurate heat map results are generated, and finally the heat map results are fused with the foreground obtained based on background difference method to reduce false positives in combustible gas detection results. The experimental results show that the proposed method has high accuracy in various scenarios, and the model can efficiently run in embedded systems, effectively solving the problem of infrared gas recognition in complex scenarios. © 2024 SPIE.

关键词： Distillation

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Application of image Recognition Algorithms based on Deep Learning in the Field of Communication Science

Application of Image Recognition Algorithms Based on Deep Le...

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2023 international conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Su, Yunxia Shanghai Publishing and Printing College Shanghai China

ISBN: (纸本)9798350315455

With the popularization of the internet, people have paid more and more attention to image processing, and deep learning has become a research hotspot. Analyzing images based on deep neural networks is a very valuable and promising direction for development. This article first introduced text representation methods and two commonly used classifiers in machine learning, namely the manual annotation model method. Then, it elaborated on the effectiveness and current application status of a fusion of multi-layer feedforward technology and convolutional neural networks for edge detection, feature extraction, and other fields. Finally, a new concept was proposed, and deep learning was applied to explain the important significance of image preprocessing in digital music file classification. Afterwards, a communication field training model based on image recognition algorithms was simulated and tested for its functionality. The test results showed that the image recognition algorithm based on deep learning had good noise processing performance during the propagation process, with an image clarity rate of over 90%. Through deep learning algorithms, automatic recognition and classification of radio signals can be achieved, including determining signal type, modulation method, frequency range, and other information. This is of great significance for radio spectrum monitoring, spectrum management, and interference source localization. By using deep learning algorithms, classification models for different types of radio signals can be trained, making the recognition of radio spectrum more accurate and reliable. © 2023 IEEE.

关键词： communication field deep learning image recognition algorithms neural networks

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