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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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Detection Scheme of Impersonation Attack Based on Sarsa(Lambda) Algorithm in Fog computing 22

Detection Scheme of Impersonation Attack Based on Sarsa(Lamb...

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proceedings of the 2022 12th international conference on Communication and Network Security

作者： Aiming Wu Shanshan Tu Junwei Liu Yihe Zhang Yongjie Yang Xuetao Bai Engineering Research Center of Intelligent Perception and Autonomous Control faculty of Information Technology Beijing University of Technology China China Mobile (Suzhou) Software Technology Co. Ltd. China Mobile (Suzhou) Software Technology Co. Ltd. China

ISBN: (纸本)9781450397520

Fog computing is a technology that provides distributed computing, storage and other services between cloud servers and Internet of things(IoT) devices. It reduces the pressure on cloud servers and utilizes network edges for computing and storage, thereby improving the performance of the entire IoT system. Since fog computing uses traditional security technologies to realize the communication between fog nodes and end users, it is inevitable for fog computing to face some security threats, such as eavesdropping attacks, camouflage attacks and other security threats. Aiming at this problem, this paper proposes a impersonation attack detection scheme based on Sarsa(lambda) algorithm. the scheme first builds a camouflage attack model and a key generation model based on physical layer security technology, then performs hypothesis testing at the receiving end to verify the legitimacy of the training signal, and then obtains the optimal threshold through the proposed algorithm. the optimal threshold realizes the terminal communication security between user nodes and fog nodes. the experimental results show that the algorithm can effectively detect camouflage attacks in a dynamic fog computing environment and have a low average detection error rate. When , AER decreased to 0.4925.

关键词： Fog computing Sarsa Impersonation Attack Physical Layer Security Hypothesis Testing

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Robust Object Detection and Tracking Model for Visually Impaired People Using Deep Convolution Neural Network Model

Robust Object Detection and Tracking Model for Visually Impa...

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Sustainable Communication Networks and Application (ICSCNA), international conference on

作者： R. Priyanka K. Vivekrabinson A. Kathijanasreen W T Chembian Atharvaa Veerasundar Srimathi S Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of CSE Kalasalingam Academy of Research and Education Krishnankoil CSE department R.M.K College of Engineering and Technology Department of CSE Vel Tech High Tech Dr. Rangarajan Dr.Sakuntala Engineering College 12th Greenwood High International High School Bengaluru Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

Visual Impairment (VI) has become the most significant challenge over the past few years. Even to complete their day-to-day activities, visually impaired persons seek assistance. A currency recognition system and automated common object could enhance the safe movement and transaction people activity with a disability. A model is generated for an object detector that could identify items for people with a disability and other significant uses by identifying them at a certain distance. the present object detection techniques need a massive quantity of training dataset that take longer time, are difficult, and it is a more complicated process. therefore, in this work, a Robust Object Detection and Tracking for Visually Impaired People using Deep Convolution Neural Network (RODT-DCNN) model is developed. the main purpose of the RODT-DCNN model lies in the accurate identification and classification of objects for VIP. the RODT-DCNN technique comprises two stages of operations such as object detection and object classification. For object detection, YOLO-v5 approach was used. Next, Elman neural network (ENN) approach can be exploited for the effectual identification and classification of objects. At last, sine cosine algorithm (SCA) can be employed for adjusting the parameters of the ENN model. the performance analysis of the RODT-DCNN approach occurs on standard dataset and the outcomes demonstrated the improved detection results of the RODT-DCNN approach in terms of diverse measures.

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Overview of the NLPCC 2023 Shared Task: Chinese Medical Instructional Video Question Answering 12th

Overview of the NLPCC 2023 Shared Task: Chinese Medical In...

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12th National CCF conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Li, Bin Weng, Yixuan Guo, Hu Sun, Bin Li, Shutao Luo, Yuhao Qi, Mengyao Liu, Xufei Han, Yuwei Liang, Haiwen Gao, Shuting Chen, Chen College of Electrical and Information Engineering Hunan University Changsha China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy Sciences Beijing China

ISBN: (纸本)9783031446986

In this paper, we present an overview of the NLPCC 2023 shared task, named Chinese Medical Instructional Video (CMIVQA), which includes three sub-tracks: temporal answer grounding in a single video, video corpus retrieval, and temporal answer grounding in video corpus. the CMIVQA datasets containing the videos, audios, and corresponding subtitles are made public, and the corresponding labels are manually annotated by medical experts. Details of the shared task, datasets, evaluation metrics, and final results will be provided in order. We hope this shared task can provide more insights into the first-aid, medical emergency, or medical education. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Medical education

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Embedded Real-time System Trusted Protection Technology for Supply Chain Scenarios 22

Embedded Real-time System Trusted Protection Technology for ...

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proceedings of the 2022 12th international conference on Communication and Network Security

作者： Jin Zhou Zhenhu Ning Qi Wang Yuchen Wang faculty of Information Technology Beijing University Of Technology China Trusted Computing Technology Co.LTD Shenzhen China

ISBN: (纸本)9781450397520

Embedded real-time operating systems are widely used in communication, industrial control and weapon intelligent control and other fields. However, due to the complex personnel and environment involved in the development of embedded systems, the development software is difficult to control independently, so it is extremely vulnerable to malicious code implantation attacks. Based on this, this paper focuses on the UCOS real-time operating system, analyzes the security threats faced by the UCOS development environment GCC in the process of precompiling, compiling, assembling and linking, and designs a plug-in-based GCC attack mode, which can implement malicious code implantation attacks on UCOS. . At the same time, a trusted protection framework for embedded real-time systems is proposed. By implementing active control and integrity measurement of firmware, operating systems and applications in embedded real-time systems, the trusted startup of embedded real-time systems is guaranteed and the supply chain stage is independently resisted. malicious code implantation attack.

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 22

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Pingping Sun Baohua Qiang Zhiguang Liu Xianyi Yang Guangyong Xi Weigang Liu Ruidong Chen Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods.

关键词： Semantic Relevance CLIP Cross-Modal Retrieval Modal Alignment

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Object Detection Algorithm Based on Coordinate Attention and Context Feature Enhancement 22

Object Detection Algorithm Based on Coordinate Attention and...

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proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Lingzhi Liu Baohua Qiang Yuanchun Wang Xianyi Yang Jubo Tian Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

In recent years, object detection has been widely used in various fields such as face detection, remote sensing image detection and pedestrian detection. Due to the complex environment in the actual scene, we need to fully obtain the feature information in the image to improve the accuracy of object detection. this paper proposes an object detection algorithm based on coordinate attention and contextual feature enhancement. We design a multi-scale attention feature pyramid network, which first uses multi-branch atrous convolution to capture multi-scale context information, and then fuses the coordinate attention mechanism to embed location information into channel attention, and finally uses a bidirectional feature pyramid structure to effectively fuse high-level features and low-level features. We also adopt the GIoU loss function to further improve the accuracy of object detection. the experimental results show that the proposed method has certain advantages compared with other detection algorithms in the PASCAL VOC datasets.

关键词： Feature pyramid network Object detection Coordinate attention Atrous convolution

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Long-short term anomaly detection in wireless sensor networks based on spatio-temporal correlation in IoT systems 23

Long-short term anomaly detection in wireless sensor network...

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proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Zhihao Wei Meng Li Jianghao Wei Industrial Big Data Laboratory Xi'an University of Posts and Telecommunications China Nanjing University of Science and Technology China

ISBN: (纸本)9798400707674

For the large number of different information sources collected by heterogeneous wireless sensor networks, traditional anomaly detection frameworks usually focus on the data analysis itself and lack a focus on unexpected sensor data, leading to unnecessary waste of resources. To address this problem, this paper proposes a method for automatic anomaly detection in heterogeneous sensor networks that combines edge data analysis and cloud data analysis. the former utilizes a completely unsupervised Isolation forest algorithm, while the cloud data analysis utilizes a Long-short term memory neural network algorithm. Experimental evaluation is performed by discussing the analysis of the proposed method using discrete and event outlier data. the experimental results show that the proposed method can reasonably cope with discrete outliers and event outliers, and the detection accuracy of event outliers is improved from 93.60% to 98.91%. At the same time, the Edge-Cloud combination alleviates to a certain extent the drawback of high energy consumption and latency of the traditional machine learning algorithm local combination model.

关键词： Anomaly detection Edge computing Isolation forests Spatio-temporal correlation

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Anomaly Handwritten Text Detection for Automatic Descriptive Answer Evaluation 22

Anomaly Handwritten Text Detection for Automatic Descriptive...

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proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Nilanjana Chatterjee Palaiahnaakote Shivakumara Umapada Pal Tong Lu Yue Lu Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Faculty of Computer Science and Information Technology University of Malaya Malaysia National Key Lab for Novel Software Technology Nanjing University China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China

ISBN: (纸本)9781450397056

Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. this paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. the method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. this result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. the distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. the proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). the results on different datasets show that the proposed work outperforms the existing methods.

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A Model robustness optimization method based on adversarial sample detection 22

A Model robustness optimization method based on adversarial ...

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proceedings of the 2022 5th international conference on Artificial Intelligence and pattern recognition

作者： Jiaze Sun Siyuan Long Xianyan Ma Yanmei Tang Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts & Telecommunications China Xi'an University of Posts & Telecommunications China

ISBN: (纸本)9781450396899

Deep neural networks are extremely vulnerable due to the existence of adversarial samples. It is a challenging problem to optimize the robustness of the model to protect deep neural networks from the threat of adversarial samples. To improve the model robustness, an integrated detection model of adversarial samples is designed to detect the existence of adversarial samples, which consists of a multi-classification detector and five single-classification detectors to perform double-layer detection of adversarial samples and intercept the adversarial samples finally sent to the image classification model. the detection experiments were conducted on the CIFAR-10 dataset for the adversarial samples generated by five attack algorithms: FGSM, BIM, DeepFool, JSMA, and C&W, and the detection success rate for all types of adversarial samples reached over 98.96%. After that, secondary attack experiments were conducted on the model, and the detection success rate of the model for the second attack adversarial samples reached over 92.36%. It provides an efficient and robust optimization method for deep neural network models in adversarial environments.

关键词： Adversarial samples Deep neural networks Integrated detection Model robustness

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Activity Attribute-Based User Behavior Model for Continuous User Authentication 22

Activity Attribute-Based User Behavior Model for Continuous ...

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proceedings of the 2022 12th international conference on Communication and Network Security

作者： Luthfi Azhari Ari Moesriami Barmawi School of Computing Telkom University INA

ISBN: (纸本)9781450397520

In general, authentication using a username and password has been used to manage access to the system. this way, it can be arranged so that only authorized people can access a system. However, it does not rule out the possibility of several spoofing attacks. User behavior can be used instead of a password to solve this problem. Given that user behavior is unique, the behavior can be used for the authentication process. One of the authentication processes is to continuously use weblogs, as proposed by Leslie et al. However, the method used by Leslie et al. still has a drawback with the Leslie method. Only the user's role is identified. So, the attacker's identity cannot be known if an attack occurs. A continuous authentication method using weblogs and user attributes such as user id, DateTime, event, category, and products is used to overcome this problem. Using this method, the user's identity can be identified immediately after the role is known. In addition, using the proposed method, the accuracy of user identity authentication obtained is, on average, 95.08%, which means it is greater than the accuracy of user role authentication using the Leslie method, which is 46%..

关键词： identity weblog authentication attribute behavior

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