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检索条件"任意字段=International Symposium on Software Engineering for Parallel and Distributed Systems"

共 10743 条记录，以下是191-200 订阅

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DNS Covert Channel Attack Detection Based on Spatio-Temporal Feature Fusion 22

DNS Covert Channel Attack Detection Based on Spatio-Temporal...

引用

22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： You, Wei Zhao, Chunlei Shan, Chao Dong, Guozhong Yu, Jie Guan, Yong Tianjin University of Technology Tianjin Key Laboratory of Intelligent Computing and Software New Technology Tianjin China Coordination Center of China Hubei Branch of National Computer Network Emergency Response Technical Team HuBei China Pengcheng Laboratory Department of New Networks Shenzhen China

ISBN: (纸本)9798331509712

An increasing number of Advanced Persistent Threat (APT) organizations are utilizing DNS (Domain Name System) covert channels to evade network intrusion detection systems and establish private authoritative servers for remote attacks. However, current detection models for DNS covert channels often have poor performance and limited generalization ability. To address these challenges, we propose a DNS covert channel attack detection method based on spatiotemporal feature fusion. This method combines the Interaction timing sequence features of DNS traffic with the spatial statistical features of domain names, and constructs a detection model called ConvSLSTM, which integrates a Convolutional Neural Network (CNN) with a stacked Long Short Term Memory (LSTM) network. CNN extracts local spatial features from traffic, while stacked LSTM captures Interaction timing sequence features, significantly improving detection accuracy. This article uses three typical public datasets for experiments, which contain a total of 440000 DNS covert channel attack data. Comparative experiments show that our method is significantly superior to existing methods and exhibits stronger generalization ability in detecting DNS covert channel attacks constructed by unknown tools. © 2024 IEEE.

关键词： Network intrusion

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QoS-aware Dynamic Service Caching and Updating in Cost-efficient Multi-Access Edge Computing 22

QoS-aware Dynamic Service Caching and Updating in Cost-effic...

引用

22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Lu, Shuaibing Jin, Xin Wu, Jie Zhou, Shuyang Wu, Shen Yang, Jackson Yan, Ran Beijing University of Technology College of Computer Science Beijing China Temple University Center for Networked Computing United States Beijing Jiaotong University School of Software Engineering Beijing China

ISBN: (纸本)9798331509712

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper proposes three novel strategies for the different stages of service caching and updating to jointly optimize the delay and cost. At the initial service caching stage, we propose a caching strategy based on dynamic programming, taking into account the constraint of limited memory resources. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on Q-learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost. © 2024 IEEE.

关键词： Markov processes

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An Extended Rough Set Model of Graph Structure for Incomplete Information System Using Spark 18

An Extended Rough Set Model of Graph Structure for Incomplet...

引用

18th international Conference on Intelligent systems and Knowledge engineering, ISKE 2023

作者： Wu, Zhengjiang Lian, Tao Li, Ruimin Wu, Xuyang School of Software Henan Polytechnic University Jiaozuo China School of Computer Science and Technology Henan Polytechnic University Jiaozuo China

ISBN: (纸本)9798350318401

In the process of data cleaning, there are problems of high difficulty and low efficiency for processing IIS (incomplete information system) that has large size data. As this problem, distributed IMR (DIMR) is a method with data preprocessing capability for distributed IIS. We propose a distributed extended rough set of graph structure (DRSG) based on DIMR using Spark to accelerate parallel processing of IIS under big data. Specifically, we introduce the concepts of reliable element and controversial element, and establish a connection through non-symmetric similarity relation. Then, a graph structure with hierarchical relationship is proposed. And the path from the reliable element to the controversial element replaces the block in DIMR to calculate the approximation set. Finally, a parallel algorithm is proposed and the experiments show that DRSG reduces the resources required for computing and improves the speed in Spark. A comparative analysis has been conducted comprehensively to assess the performance of the proposed algorithm with DIMR on 8 datasets. And the results show that DRSG has faster speed. And the dependency is similar to or higher than DIMR. © 2023 IEEE.

关键词： Rough set theory

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Beat Frequency Oscillation Analysis for a DC Microgrid with Multiple Boost Converters 14

Beat Frequency Oscillation Analysis for a DC Microgrid with ...

引用

14th IEEE international symposium on Power Electronics for distributed Generation systems, PEDG 2023

作者： Xu, Haoge Zhu, Yuxin Lin, Zhengyu Loughborough University School of Mechanical Electrical and Manufacturing Engineering Leicestershire United Kingdom

ISBN: (纸本)9798350328233

In general, energy sources and electronics loads are connected to DC microgrids via power electronic converters. However, interactions of these power converters may introduce instability issues into the system. One typical problem is the generation of the beat frequency oscillation caused by the minor differences of switching frequencies between power converters. This paper analyses the output terminal characteristics of the Boost converter, and a small signal model is built to analyse the beat frequency oscillation between two parallel-connected Boost converters. Based on the analysis, a hybrid method is proposed to alleviate the beat frequency oscillation. Simulation and experiments results are presented to show the effectiveness of the proposed solution. © 2023 IEEE.

关键词： Boost converter

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Ensemble Distillation for Out-of-distribution Detection 29

Ensemble Distillation for Out-of-distribution Detection

引用

29th IEEE international Conference on parallel and distributed systems, ICPADS 2023

作者： Wang, Dongdong Xu, Jingyao Lu, Siyang Wei, Xiang Wang, Liqiang University of Central Florida Department of Civil Environmental and Construction Engineering OrlandoFL United States Beijing Jiaotong University School of Computer and Information Technolog Beijing China Beijing Jiaotong University School of Software Engineering Beijing China University of Central Florida Department of Computer Science OrlandoFL United States

ISBN: (纸本)9798350330717

Out-of-distribution detection is critical to a reliable application of deep neural networks. To reduce model uncertainty, we propose a simple yet effective approach of ensemble knowledge distillation. We blend ensemble model and knowledge distillation to improve model generalization on indomain recognition, thereby yielding accurate and robust out-of-distribution detection. The former effectively expands data recognition feature space, while the latter further regularizes the model through knowledge distillation, enhancing in-domain feature recognition. This effective integration successfully yields lower model uncertainty on in-domain feature recognition and improves anomaly detection in a more scalable manner. We justify our approach through extensive experiments on various benchmarks, demonstrating its significant improvement in out- of-distribution detection. We validate our approach with a variety of up-to-date DNNs, like Vision Transformer. © 2023 IEEE.

关键词： Data Augmentation Ensemble Learning Knowledge Distillation Out-of-distribution Detection

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Coded parallelism for distributed Deep Learning

Coded Parallelism for Distributed Deep Learning

引用

2023 IEEE international symposium on Information Theory, ISIT 2023

作者： Ji, Songting Zhang, Zhaoyang Yang, Zhaohui Jin, Richeng Yang, Qianqian College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China Zhejiang University International Joint Innovation Center Haining314400 China

ISBN: (纸本)9781665475549

With the rapid development of deep learning, the parameters of modern neural network models, especially in the field of Natural Language Processing (NLP) are extremely huge. When the parameters of the model are larger even than the storage memory of a single device, it is necessary to split the original big learning model into different parts with each part assigned to one device, thus realizing joint model training over different devices (i.e., distributed training). In this paper, we aim to introduce the advanced coding scheme into the distributed parallel framework, which leads to the perfect combination of coding and the underlying calculation of neural networks. The proposed scheme is not only able to avoid the impact of poor computing power or low bandwidth and even dropped devices (stragglers) on system performance but also reduce the communication load between different devices, thereby greatly improving the performance of distributed parallel systems. © 2023 IEEE.

关键词： Deep learning

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ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicular CAN Bus Networks 33

ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicula...

引用

33rd Euromicro international Conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Rendel, Jonathan Balte, William Kurunathan, Harrison Ali, Hazem Ismail Roque, Alexandre Dos Santos Morais, Wagner Ourique De Fazeli, Mahdi Halmstad Sweden Polytechnic Institute of Porto CISTER/ISEP Portugal Federal University of Rio Grande do Sul - UFRGS Graduate Program of Electrical Engineering Porto Alegre Brazil

ISBN: (纸本)9798331524937

Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called "ZeroCAN"which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over 99% and a false positive rate below 0.01% during normal operation in most cases. © 2025 IEEE.

关键词： Anomaly detection CAN bus IDS Feature engineering Zero-day

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Enhanced YOLOv7 Model for Aerial Drone Detection in Complex Environments 22

Enhanced YOLOv7 Model for Aerial Drone Detection in Complex ...

引用

22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Fu, Changlong Wu, Yasu Jin, Xin Wang, Puming Jiang, Qian Software College Yunnan University Kunming China Software College Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798331509712

With the growing popularity of Unmanned Aerial Vehicles (UAVs) in civilian, commercial, and military applications, the need for robust drone detection systems has become increasingly urgent. However, due to the small size of drones when observed from a distance, most traditional machine learning and two-stage deep learning detection methods currently struggle to capture effective feature information of drones in complex image backgrounds, and often fail to meet the requirements for real-time performance. To address this challenge, we have introduced an improved version of the YOLOv7-Tiny model, known as YOLOv7-ADD, which significantly enhances the detection performance of small target drones at various distances and under complex backgrounds. The model integrates the Scylla-IoU (SIoU) loss function to improve the accuracy of bounding box regression and employs the BiFormer attention mechanism, which dynamically focuses on key features of drones within the detection scene, enhancing the model's recognition capabilities for drones. Furthermore, the introduction of the Diverse Branch Block (DBB) helps the model capture multi-scale features, optimizing the detection effect for drones of various sizes. Extensive experiments on drones dataset have demonstrated the superior performance of YOLOv7-ADD. It achieved a 2.7% improvement in mAP@0.5 and a 1.1% increase in mAP@0.5:0.95, while maintaining high FPS detection performance. This provides an efficient drone detection solution for aerial surveillance. The code is available on https://***/FuChanglong/***. © 2024 IEEE.

关键词： Target drones

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TILDE: Topic-Tracking Infrastructure for Dynamic Message Latency and Deadline Evaluator for ROS 2 Application 27

TILDE: Topic-Tracking Infrastructure for Dynamic Message Lat...

引用

27th IEEE/ACM international symposium on distributed Simulation and Real Time Applications, DS-RT 2023

作者： He, Xuankeng Sato, Hiromi Okumura, Yoshikazu Azumi, Takuya Graduate School of Science and Engineering Saitama University Japan Saitama University Faculty of Engineering Japan Research Institute of Systems Planning Inc Japan

ISBN: (纸本)9798350337846

Autonomous-driving systems require real-time processing for safety. Detecting the deadline miss (disabling to execute the process until the designated time) of autonomous-driving software is essential for executing real-time processing. We proposed the application of a dynamic message tracking system (Topic-tracking Infrastructure for Dynamic Message Latency and Deadline Evaluator) to software based on Robot Operating System (ROS) 2, e.g., Autoware (autonomous-driving software). TILDE transmits MessageTrackingTag with ROS 2 messages to trace and dynamically specify the data flow of the messages. In particular, MessageTrackingTag contains input and output times at each node with the data flow. The information of the MessageTrackingTag is acquired by a deadline detector to identify the time from the input to output (hereafter latency) of the data flow. The latency is compared to the deadline to detect the occurrence of a deadline miss. In principle, the deadline detector is a package of TILDE for notifying deadline misses. In addition, this paper conducted two evaluations of TILDE. First, we measured the overhead caused by embedding TILDE on ROS 2 nodes through the transmission of a String-type message from one node to another. Second, we measured the deadline miss detection rate using CARET (a latency analysis tool for autonomous-driving systems) to validate the accuracy of the deadline detector in determining deadline misses. © 2023 IEEE.

关键词： Real time systems

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Intelligent Recognition of Antigen Detection Reagent for Corona virus based on Improved Text Recognition 29

Intelligent Recognition of Antigen Detection Reagent for Cor...

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29th IEEE international Conference on parallel and distributed systems, ICPADS 2023

作者： Han, Jin Wang, Tianhao Deng, Zhanman Huang, Bingbing Shao, Linbo Zhou, Xia Nanjing University of Information Science and Technology College of Software Nanjing China Hu'Nan Meteorological Disaster Prevention Technology Center College of Software Changsha China Jinlinglnstitute of Technology School of Mechanical Engineering Nanjing China

ISBN: (纸本)9798350330717

In the wake of the post-epidemic era, the gradual standardization of coronavirus detection has given rise to concerns about the efficiency of corona virus self-test result identification. This study primarily employs a scene text recognition technology based on DBNet and SVTR to discern the outcomes of corona virus detection. The methodology involves locating and recognizing the C-line and T-line within images of corona virus antigen detection reagents. By rectifying image positioning, the text region is initially identified and screened using a DBNet-based text recognition algorithm. Subsequently, the SVTR model is employed to pinpoint key text elements. The detection area is assessed through text-based positioning, followed by color extraction and straight line detection to ascertain the ultimate result. Experimental findings underscore the high recognition efficiency and accuracy of the DBNet and SVTR models in this context, suggesting their potential for widespread adoption. © 2023 IEEE.

关键词： Association recognition Deep learning Detection of novel corona virus antigen Image Detection Scene Text Detection

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