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

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

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

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Euromicro Conference on parallel, distributed and Network-Based Processing

作者： Jonathan Rendel William Balte Harrison Kurunathan Hazem Ismail Ali Alexandre Dos Santos Roque Wagner Ourique De Morais Mahdi Fazeli Department for Computing and Electronics for Real-time and Embedded Systems (CERES) School of Information Technology (ITE) Halmstad University Halmstad Sweden CISTER/ISEP Polytechnic Institute of Porto Portugal Graduate Program of Electrical Engineering Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

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 $\mathbf{9 9 \%}$ and a false positive rate below $\mathbf{0. 0 1 \%}$ during normal operation in most cases.

关键词： Support vector machines Telecommunication traffic Feature extraction Data models Timing Security Vehicle dynamics Anomaly detection Standards Payloads

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Decentralized model predictive control for autonomous robot swarms with restricted communication skills in unknown environments 14

Decentralized model predictive control for autonomous robot ...

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14th international symposium on Intelligent systems

作者： Puzicha, Alexander Buchholz, Peter Tech Univ Dortmund Emil Figge Str 61 D-44227 Dortmund Germany

The paper presents a new approach and a related real-time parallel simulation tool for modeling and analyzing a swarm of more than 60 distributed autonomous mobile robots communicating over an unreliable and capacity restricted wireless communication network. It includes a physical simulation of static obstacles, dynamic obstacles with scriptable movement, soil condition, active jammers, static and dynamic link obstacles with configurable damping as well as thermal noise. The simulated ground based mobile robots use CPBP [12] as probabilistic model predictive closed loop controller in combination with gradient free cost functions to evaluate complex unsteady goodness aggregations. The goal of this approach is the development of connection aware swarm behavior for complex missions such as terrain exploration, formations, convoy escorting or creation of a mobile ad hoc network in dynamic disaster areas under realistic environmental conditions. The missions can be combined in any manner and the target extraction of the high-level commands for each agent is done implicitly. To validate the developed behavior, the independent software control kernel is able to be used on real robots as well. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Real-Time Simulations parallel Simulations Simulation-Based Virtual Environments Swarm Robotics Model Predictive Control Autonomous Robots Swarm Behavior

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A coarse-to-fine parallelizable surface defect detection approach for railway trackside equipment

A coarse-to-fine parallelizable surface defect detection app...

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international Conference on parallel and distributed systems (ICPADS)

作者： Guanjia Zhang Weiwei Xing Shuzhong Yang Weibin Liu Xiang Wei Jian Zhang Shunli Zhang School of Software Engineering Beijing Jiaotong University Beijing China Infrastructure Inspection Research Institute China Academy of Railway Sciences Corporation Limited Beijing China Institute of Information Science Beijing Jiaotong University Beijing China

Surface defects of railway trackside equipment pose a serious risk on the safety of railway transportation systems. Image-based surface defect detection methods have made significant progress. However, the image background of trackside equipment is complex, and there is a large amount of noise, which makes existing methods inadequate in accurately detecting small surface defect regions. To tackle with this issue, we propose a coarse-to-fine parallelizable surface defect detection approach to hierarchically detect the defects of trackside equipment. Firstly, a detection network is designed to locate and extract trackside equipment, which aims at roughly focusing the detection field from the original image to the region of interest of individual trackside equipment. Then, a novel semantic segmentation network is proposed to segment the major components of trackside equipment, so as to further finely focus on the defect regions. We apply multiple segmentation networks to parallelly segment various trackside equipment. In the segmentation network, a dense feature enhancement method is introduced to strengthen the high-level semantic information, and a feature partitioning enhancement strategy is designed to improve the segmentation performance for small defect regions. Finally, according to the visual characteristics of the segmentation output, we propose a defect recognizer to discriminate the defects. Extensive experimental results demonstrate that the proposed surface defect detection approach achieves higher accuracy for trackside equipment.

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Ef-OFDP: An Efficient OpenFlow-based Topology Discovery Protocol for SDN

Ef-OFDP: An Efficient OpenFlow-based Topology Discovery Prot...

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Computer Applications and Information Technology (ISCAIT), international symposium on

作者： Haitao Li School of Information Engineering Liaoning Vocational University of Technology Jinzhou China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

To address the issues of high control plane load, excessive redundant traffic, and topology update delays in traditional topology discovery protocols in SDN, this paper proposes an optimized mechanism, Ef-OFDP. By integrating the Minimum Vertex Cover (MVC) algorithm and Bidirectional Forwarding Detection (BFD) technology, Ef-OFDP constructs a dual-layer probing architecture comprising covering switches and ordinary switches, and integrates BFD to enable dynamic awareness of link states and trigger events, thereby reducing the overhead of periodic LLDP packets. Simulation experiments validate Ef-OFDP’s performance in heterogeneous topologies (tree, linear, Fat-Tree). Results demonstrate that compared to OFDP and OFDPv2, Ef-OFDP achieves over 50% efficiency gain in total volumes of Packet-Out and Packet-In messages, with even more pronounced benefits in large-scale networks. The solution in this paper provides a low-overhead and highly responsive topology discovery method for SDN scalability bottlenecks, suitable for dynamic scenarios such as data centers and distributed edge networks.

关键词： Protocols Network topology Heuristic algorithms Computer architecture Control systems Topology Sensors software defined networking Tuning Testing

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The Fast and Scalable MPI Application Launch of the Tianhe HPC system

The Fast and Scalable MPI Application Launch of the Tianhe H...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Yiqin Dai Yong Dong Min Xie Kai Lu Ruibo Wang Mingtian Shao Juan Chen College of Computer National University of Defense Technology Changsha China

Fast and scalable MPI application launch helps achieve exascale performance and is becoming a common goal in high-performance computing. However, the traditional launch technique suffers from scalability deficiencies in the global information exchange and the global barrier operation. This drawback makes it challenging to launch MPI applications quickly in large-scale systems. In this paper, we propose a fast and scalable application launch technique and details its associated hardware and software support. The optimized launch technique includes a locality-aware static address generation rule for eliminating the need for address exchange and a topology-aware global communication scheme for improving global communication efficiency. We also propose an optimized application launch sequence for supporting the above launch technique. We implement and evaluate the proposed launch technique on the Tianhe-2A supercomputer and the Tianhe Exascale Prototype Upgrade System. Experimental results show that our technique can reduce the launch time by 26.1% when launching an application with 256K processes.

关键词： Costs Scalability High performance computing Prototypes Supercomputers software Hardware

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A Design and Implementation of Decentralized Edge Intelligent LoRa Gateway

A Design and Implementation of Decentralized Edge Intelligen...

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international symposium on Object-Oriented Real-Time distributed Computing

作者： Xiaobo Zhang Zhangqin Huang Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

Abstract-As a narrowband communication technology, Long Range (LoRa) contributes to the long development of Internet of Things (IoT) applications. The LoRa gateway plays an important role in the IoT transport layer, and security and efficiency are the key issues of current research. On the one hand, in the centralized working model of IoT systems built by traditional LoRa gateways, all the dat a generated and reported by end devices are processed and stored in cloud servers, which are susceptible to security issues such as data loss and data falsification. On the other hand, edge computing, as an innovative approach that brings data processing and storage closer to the endpoints, can create a decentralized security infrastructure for LoRa gateway systems, resulting in an edge intelligent IoT working model. Although this paradigm delivers unique features and improved quality of service (QoS), installing IoT applications at LoRa gateways with limited computing and memory capabilities presents considerable obstacles. To address this challenge, an edge intelligent LoRa gatew ay is designed and implemented in this paper. Firstly, we developed an edge intelligent LoRa gateway prototype on an FPGA-based embedded hardware system. Then, we proposed Latency-Aware Algorithm (LAA) can greatly improve the reliability of the network system by using a distributed edge computing network technology that can achieve maintenance operations such as detection, repair, and replacement of failures of edge nodes in the network. Finally, many experiments were conducted to evaluate the performance of the proposed edgeintelligent LoRa gat eway. The results indicate that the proposed edge intelligent LoRa gateway is more effective in latency-aware in IoT applications, while greatly ensuring system availability and IoT network reliability.

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SecFortress: Securing Hypervisor using Cross-layer Isolation

SecFortress: Securing Hypervisor using Cross-layer Isolation

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international symposium on parallel and distributed Processing (IPDPS)

作者： Qihang Zhou Xiaoqi Jia Shengzhi Zhang Nan Jiang Jiayun Chen Weijuan Zhang Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Metropolitan College Boston University USA

Virtualization is the corner stone of cloud computing, but the hypervisor, the crucial software component that enables virtualization, is known to suffer from various attacks. It is challenging to secure the hypervisor due to at least two reasons. On one hand, commercial hypervisors are usually integrated into a privileged Operating System (OS), which brings in a larger attack surface. On the other hand, multiple Virtual Machines (VM) share a single hypervisor, thus a malicious VM could leverage the hypervisor as a bridge to launch “cross-VM” attacks. In this work, we propose SecFortress, a dependable hypervisor design that decouples the virtualization layer into a mediator, an outerOS, and multiple HypBoxes through a cross-layer isolation approach. SecFortress extends the nested kernel approach to de-privilege the outerOS from accessing the mediator's memory and creates an isolated hypervisor instance, HypBox, to confine the impacts from the untrusted VMs. We implemented SecFortress based on KVM and evaluated its effectiveness and efficiency through case studies and performance evaluation. Experimental results show that SecFortress can significantly improve the security of the hypervisor with negligible runtime overhead.

关键词： Performance evaluation distributed processing Virtual machine monitors Runtime Prototypes Virtual machining software

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ESSA 2022 Keynote Speaker: Keep Your Composure: HPC, Data Services, and the Mochi Project

ESSA 2022 Keynote Speaker: Keep Your Composure: HPC, Data Se...

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Rob Ross Mathematics and Computer Science Division Argonne National Laboratory

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

For decades high-performance computing (HPC) systems have been technology leaders in utilization of massive concurrency, in no small part because HPC applications are composed of software components that provide only the communication, concurrency, and synchronization needed for the task at hand. The storage community has likewise benefited from adopting this philosophy in recent years. The composable storage technology produced by the Mochi team is now widely leveraged in the DOE community as well as industry products, and academic and international research projects. In this talk we will cover the motivation for and roots of the Mochi framework, a variety of ways these tools are employed in HPC today, and consider new challenges to be tackled to extend the capabilities and reach of data services in HPC.

关键词： High performance computing Conferences Concurrent computing Computer science Task analysis System software Synchronization

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ADCME MPI: distributed machine learning for computational engineering

ADCME MPI: Distributed machine learning for computational en...

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AAAI 2021 Spring symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, AAAI-MLPS 2021

作者： Xu, Kailai Darve, Eric Institute for Computational and Mathematical Engineering Stanford University StanfordCA94305 United States Mechanical Engineering Stanford University StanfordCA94305 United States

We propose a framework for training deep neural networks (DNNs) that are coupled with partial differential equations (PDEs) in a parallel computing environment. Unlike most distributed computing frameworks for DNNs, our focus is to parallelize both numerical solvers and DNNs in forward and adjoint computations. Our parallel computing model views data communication as a node in the computational graph for numerical simulations. The advantage of our model is that data communication and computing are cleanly separated, which enables better flexibility, modularity, and testability of the software. We demonstrate our approach on a large-scale problem and show that we can achieve substantial acceleration by using parallel numerical PDE solvers while training DNNs that are coupled with PDEs. Copyright © 2021for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： Deep neural networks

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A Hardware-Friendly Alternative to Softmax Function and Its Efficient VLSI Implementation for Deep Learning Applications

A Hardware-Friendly Alternative to Softmax Function and Its ...

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IEEE international symposium on Circuits and systems (ISCAS)

作者： Meng-Hsun Hsieh Xuan-Hong Li Yu-Hsiang Huang Pei-Hsuan Kuo Juinn-Dar Huang Department of Electronics and Electrical Engineering & Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

The Softmax function holds an essential role in most machine learning algorithms. Conventional realization of Softmax necessitates computationally intensive exponential operations and divisions, thereby posing formidable challenges in developing low-cost hardware implementations. This paper presents a promising hardware-friendly alternative, Squaremax, which gets rid of complex exponential operations. The function definition is extremely simple and can thus be efficiently implemented in both software and hardware. Experimental results show that Squaremax consistently attains comparable or superior accuracy over several popular models. Besides, this paper also proposes an efficient hardware architecture design of Squaremax. It requires no functional units for exponential and logarithmic operations, and is even lookup table (LUT) free. It adopts a flexible 16-bit fixed-point Q format for I/O to better preserve the output precision, which leads to higher model accuracy. Moreover, it yields substantial improvements in speed, area, and power, as well as achieves remarkable area and power efficiency of 664 G/mm 2 and 1396 G/W in a 40nm process. Therefore, hardware-friendly Squaremax is a very promising alternative to complex Softmax in both software and hardware for deep learning applications, and the proposed hardware architecture design and efficient LUT-free implementation do achieve a notable improvement in speed, area, and power.

关键词： Deep learning Accuracy software algorithms Computer architecture Very large scale integration parallel processing Transformers

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