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An Intrusion Detection System Using vision Transformer for Representation Learning 6th

An Intrusion Detection System Using Vision Transformer for R...

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6th international conference on Frontiers in Cyber Security (FCS)

作者： Ban, Xinbo Liu, Ao He, Long Gong, Li CAAC Res Inst 2 Chengdu 610042 Peoples R China

ISBN: (纸本)9789819993307;9789819993314

Intrusion Detection System (IDS) is important in safeguarding cybersecurity by identifying and responding to malicious activities. Traditional IDSs filter the abnormal traffic through rules or learn the behaviors of normal and abnormal network data. Nevertheless, these methods utilize the manually designed feature set that introduces limitations in this field. Machine learning shows advantages in the traffic classification domain but still faces challenges of computing resource consumption and a high false positive rate. This paper presents an innovative approach to lightweight IDS using vision transformer techniques for feature representation learning in the context of network intrusion detection. Specifically, our IDS uses a self-attention mechanism to process network traffic, which flattens the splitted network flow to images for training the model. It utilizes Natural Language processing techniques to capture temporal-spatial information from network traffic. We conduct several experiments to show the effectiveness of our proposed method. The results show that our approach can achieve high accuracy in intrusion detection tasks and keep the false positive rate very low at the same time. The findings highlight the potential of vision transformers in IDS and contribute to the development of robust network security solutions for critical domains like civil aviation.

关键词： Intrusion detection system vision Transformer Representation learning Network anomaly detection Traffic classification

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Application of Particle Detection Methods to Solve Particle Overlapping Problems

Application of Particle Detection Methods to Solve Particle ...

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3rd international conference on image processing and vision Engineering, IMPROVE 2023

作者： Alkendi, Wissam Mahapatra, Paresh Alkindy, Bassam Guyeux, Christophe Barthés, Magali UTBM CIAD BelfortF-90010 France FEMTO-ST Institute Univ. Franche-Comté CNRS 15 B Avenue des Montboucons Besançon France Department of Computer Science College of Science Mustansiriyah University Baghdad10052 Iraq

ISBN: (纸本)9789897586422

The study of fluid flows concerns many fields (e.g., biology, aeronautics, chemistry). To overcome the problems of flow disturbances caused by intrusive physical sensors, different methods of flow quantification, based on optical visualization, are particularly interesting. Among them, PTV (Particle Tracking Velocimetry) which allows the individualized tracking of tracers/particles, is of growing interest. Different numerical treatments will enable us to identify and track the particles. However, detection algorithms (e.g., Sobel, Canny, Robert, Gaussian, morphology) can be sensitive to noise and the phenomenon of overlapping particles in flow. In this work, we have focused on the detection part with the objective of improving it as much as possible. To quantify the performance of the different methods tested, synthetic images, with well-defined parameters have been generated. We compared the performances of the Laplacian of Gaussian (LoG) and the Difference of Gaussian (DoG) methods, with the traditional method of threshold binarization. In addition, we tested other techniques based on non-local means (NLM) and overlapping detector to improve the detection of particles in case of noisy images or overlapping particles. The results show that the LoG gives very good results in most cases, with additional improvement when using the NLM and the overlap detector. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Laplace transforms

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Advanced Machine Learning Detection using UAV Motion Tracking and Control 2

Advanced Machine Learning Detection using UAV Motion Trackin...

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2nd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Bala, Kiran Verma, Amar S. Chandigarh Engineering College Department of Computer Science & Engineering Jhanjeri India Vivekananda Global University Department of Computer Science Engineering Jaipur India

ISBN: (纸本)9798350347456

Drones, or unmanned aerial vehicles (UAVs), have become more commonplace thanks to the booming commercial UAV sector. The potential for these devices to do significant harm, whether on purpose or by accident, has prompted urgent security concerns. In recent years, several solutions have been presented by academics and businesses to the problem of protecting vital infrastructure. Because to its superior resilience, computer vision is often employed as an autonomous drone detection solution over other offered alternatives such as RADAR, acoustics, and RF signal analysis. As deep learning algorithms are among the most efficient of these computer vision-based methods, they are increasingly being used in practise. In this work, we offer a self-sufficient drone tracking and identification system that makes use of a stationary beam profile and a lesser camera on a movable turret. In order to make the most optimal use of both resources, we suggest a combined multi-frame machine learning detection approach, where the frame from the zoomed turret camera is superimposed on the frame from the wide-angle static camera. With this method, we are capable of building a cost-effective pipeline that allows for the simultaneous identification of tiny sized aerial invaders on both the main picture plane and the magnified image plane. In addition, we provide the whole system, which includes tracking techniques, deep learning classification frameworks, and protocols. © 2023 IEEE.

关键词： Aircraft detection

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Error Resilient Approximate Restoring Divider for image processing Application 20

Error Resilient Approximate Restoring Divider for Image Proc...

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20th IEEE India Council international conference, INDICON 2023

作者： Srujana, H.M. Sudarshan, Deeksha Seethur, Rashmi Pes University Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350305593

The number of transistors and the complexity of the circuits both rise as Very Large-Scale Integration (VLSI) technology progresses, which leads to an increase in the amount of power that is required. Due to the wave borrow subtraction that occurs in each row of partial remainder computation, division is a more complicated mathematical operation than addition or multiplication. Computing by approximation is gradually becoming the norm for producing compact, economical, and high-speed solutions, particularly in architectures that are tolerant of mistakes to some level. As a result of the persistence of vision, even minute differences in the results of image and video processing apps go undetected. As a result, applications of this kind provide the optimal setting in which to put approximation computing into practice. The image division application is going to be performed using a 16/8 division module, which is proposed in this study. Approximate Divider Cell ADC1, ADC2, ADC3 and ADC4 are modified versions of the actual cell that makes up the division module. This was done in order to minimize the space and power consumption of the overall design. ADC1, ADC2, ADC3 and ADC4 show area and power improvement of (23.82%, 19.68), (17.41%, 48.56%), (26.82%, 48.88%) and (13.03%, 14.36%) respectively, for approximation factor 8, when compared to exact restoring divider. © 2023 IEEE.

关键词： Approximate Computing Approximate Divider Cell Error Resilient Peak signal-to-noise ratio Very Large-Scale Integration

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Retrieved Generative Captioning for Medical images 23

Retrieved Generative Captioning for Medical Images

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20th international conference on Content-Based Multimedia Indexing (CBMI)

作者： Beddiar, Djamila Romaissa Oussalah, Mourad Seppanen, Tapio Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland Univ Oulu Fac Med Oulu Finland

ISBN: (纸本)9798400709128

Understanding the content of medical images and mapping it into text is a very trending topic in intersection of two main domains;computer vision and natural language processing. This is known as medical image captioning, which plays a vital role in developing automatic systems for diagnosis purposes. Recent research in the medical field provided promising results for both deep-learning based and retrieval-based models for image captioning. However, each one of them has its own drawbacks, that can be overcome if combined. In addition, existing diagnosis systems are still not able to provide enough explanation about the findings, which might be similar to what a physician can deliver. In this regard, we present in this paper a combination of a generative deep-learning based method and a retrieval-based model for medical image captioning. First, we train an attention-based encoder-decoder model to generate new captions for given medical images. Then, we fit the generated caption from the generative model to the retrieval-based model, which retrieves the most similar caption from the training database. This multi-stage approach allows us to generate most important words of the caption (with the generative model) and then search for the most close caption that includes such words (with the retrieval-based model). Another way of combining both models is by selecting at each time the caption with highest score among generated and retrieved captions. We evaluate our proposed model on the medical ROCO dataset for which we achieved a BLEU-4 score of 07.89 for the radiology class and 03.19 for the out-of-class data, for the multi-stage model. Similarly, best results were achieved for the fused model (predicted caption is the best among generated and retrieved) where we obtain a BLEU-4 values of 18.61 for the radiology class and 13.28 for the out-of-class data. Even though our results seem to be low, they outperformed the state-of-the-art results on the same dataset and could

关键词： image Captioning Medical images Neural Networks Retrievalbased Captioning Generative-based Captioning

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Increasing the Accuracy and Speed of Digital Gram Gauges Calibration through the AI Use 27

Increasing the Accuracy and Speed of Digital Gram Gauges Cal...

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27th international conference on Soft Computing and Measurements, SCM 2024

作者： Sushnikov, Victor A. Semenov, Konstantin K. Elgram Llc St. Petersburg Russia Higher School of Computer Tech. and Information Systems Peter The Great St. Petersburg Polytechnic University St. Petersburg Russia

ISBN: (纸本)9798350363708

The paper presents a new approach to organising verification and calibration of gram gauges through artificial intelligence tools' use, which improves the accuracy and speed of performing relevant metrological procedures. The article presents the structure of the corresponding verification and calibration setup for gram gauges, which, for the first time, uses computer vision. The paper describes the main stages of the performed mathematical processing using neural networks. The authors discuss the advantages and disadvantages of the proposed approach in comparison with the standard procedure for testing existing commercially produced gram gauges. © 2024 IEEE.

关键词： Calibration

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An Edge-oriented Deep Learning Model Security Assessment Framework 21

An Edge-oriented Deep Learning Model Security Assessment Fra...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Pengbo Zhang, Boyin Zhang, Shigeng Liu, Xuan Central South University School of Computer Science and Engineering ChangSha China Hunan University College of Computer Science and Electronic Engineering ChangSha China

ISBN: (纸本)9798350329223

As computer performance advances and deep learning models proliferate, their applications expand, but concerns arise due to their opaque nature and security issues. Edge intelligence requires more from these models but faces limitations like insufficient computing power and memory. Existing research on model compression mainly focuses on the trade-off between compression and accuracy, neglecting deployment at the edge and its impact on factors beyond accuracy. Furthermore, evaluation methods for model performance and robustness in compression techniques are underdeveloped. To address these issues, this paper introduces a comprehensive framework for evaluating edge intelligence deep learning models. It conducts a thorough assessment of these models on common image classification datasets, comparing their performance with three compression algorithms and analyzing their security implications. The proposed framework aims to provide a holistic assessment of deep learning models, promoting trustworthy edge intelligence, enabling secure deployments, and enhancing understanding of model decision-making processes. © 2023 IEEE.

关键词： Classification (of information)

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Comprehensive Survey On Remote Sensing image processing Techniques for image Classification 12th

Comprehensive Survey On Remote Sensing Image Processing Tech...

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12th EAI international conference on Context-Aware Systems and Applications, ICCASA 2023

作者： Tran, Thuy Thi Huynh, Hiep Xuan Faculty of Information Technology – Communication University of CuuLong Vinh Long Viet Nam College of Information and Communication Technology Can Tho University Can Tho Viet Nam

ISBN: (纸本)9783031588778

Remote sensing technology is now being used with unprecedented high resolution, making important contributions in practical applications such as urban development, construction planning and disaster prediction. However, although many scholars have studied algorithms for remote sensing image processing, there have not been detailed summary articles to support new researchers in this field. In this paper, we present an overview of remote sensing images, types of remote sensing satellite images and related studies. Next, we briefly review the recent history of remote sensing image processing techniques. Then, list related studies on remote sensing image classification. Finally, based on the current status of research on remote sensing images, we propose some future research directions in order to provide survey references for new studies in the field of remote sensing. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Urban planning

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image Classification Using Federated Learning 6th

Image Classification Using Federated Learning

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6th international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

作者： Byeon, Haewon Ullah, Ajdar Syed, Ziauddin Siddiqui, Alighazi Aftab, Aasif Sarfaraz, Mohd Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of School of Mechanical and Manufacturing Engineering National University of Science and Technology Islamabad44000 Pakistan Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan45142 Saudi Arabia Department of Information Technology and Security College of Computer Science and Information Technology Jazan University Jazan45142 Saudi Arabia

ISBN: (纸本)9783031530814

Multiple users can jointly train a universal model using federated learning, a revolutionary AI approach, without having to reveal their personal data. With this strategy, anonymity is maintained while a secure learning environment is guaranteed. In contrast to the Workplace 31 dataset, this study presents a brand-new dataset made up of 23,326 photos obtained from eight different corporate sources and painstakingly organised into 31 classifications. It is noteworthy that this dataset is the first image classification dataset created specifically for federated learning. Along with the dataset, we also suggest the revolutionary federated learning algorithms Handled Cyclic and Handled Star. A cycle progression is established via Handled cycle, whereby a user gets weights from a previous user, adjusts them through localised training, and then transmits them to the following user. The essential job of image categorization within the context of computer vision is considerably improved by this novel approach, which highlights the development of federated learning. Traditional centralised deep learning model training requires centralised access to enormous datasets, which raises questions about data security and privacy. Federated learning addresses these issues by facilitating dispersed device collaboration for model training while restricting raw data exposure. This paper highlights the crucial role that federated learning has had in furthering AI research by giving an overview of its use in the context of image categorization. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image classification

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MIS-Net: A Deep Residual Network Based on Memorised Pooling Indices for Medical image Segmentation 1

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7th international conference on computer vision and image processing, CVIP 2022

作者： Emerson, Nithiyaraj E. Selvaraj, Arivazhagan Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamil Nadu Sivakasi626005 India

ISBN: (数字)9783031314070

ISBN: (纸本)9783031314063

Deep learning has recently become vitally important in the field of medical image segmentation. Segmentation architectures include more network layers than classification architectures and require roughly twice as many network parameters. This large number of network layers may result in vanishing gradient or redundant computation, increased computational complexity and more memory consumption. Therefore, it is essential to develop an efficient deep model for segmenting medical images. In this work, a novel deep model named MIS-Net is proposed for medical image segmentation. The proposed MIS-Net model is a 69-layer encoder/decoder convolutional model that employs the concept of memorised pooling indices and residual learning. The proposed model is examined for three medical segmentation challenges, namely liver segmentation from the LITS and 3DIRCADb datasets, lung and COVID-19 infection segmentations from the COVID-19 CT dataset. The experiment results for the segmentation of the liver, lung, and COVID-19 infection acquire average dice scores of 0.989, 0.992, and 0.934, respectively. The MIS-Net model surpasses the other deep segmentation models with 0.34M (million) network parameters. The proposed model is a parameter efficient model that reduces the computational complexity and memory utilisation of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Network layers

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