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检索条件"任意字段=13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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AutoSec: Multidimensional Timing-Based Anomaly Detection for Automotive Cybersecurity 26

AutoSec: Multidimensional Timing-Based Anomaly Detection for...

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ieee 26th international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Tepic, Milan Abdelaal, Mohamed Weber, Marc Rothermel, Kurt Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany Vector Informat GmbH Stuttgart Germany

ISBN: (纸本)9781728144030

Nowadays, autonomous driving and driver assistance applications are being developed at an accelerated pace. this rapid growth is primarily driven by the potential of such smart applications to significantly improve safety on public roads and offer new possibilities for modern transportation concepts. Such indispensable applications typically require wireless connectivity between the vehicles and their surroundings, i.e. roadside infrastructure and cloud services. Nevertheless, such connectivity to external networks exposes the internal systems of individual vehicles to threats from remotely-launched attacks. In this realm, it is highly crucial to identify any misbehavior of the software components which might occur owing to either these threats or even software/hardware malfunctioning. In this paper, we introduce AutoSec, a host-based anomaly detection algorithm which relies on observing four timing parameters of the executed software components to accurately detect malicious behavior on the operating system level. To this end, AutoSec formulates the task of detecting anomalistic executions as a clustering problem. Specifically, AutoSec devises a hybrid clustering algorithm for grouping a set of collected timing traces resulted from executing the legitimate code. During the runtime, AutoSec simply classifies a certain execution as an anomaly, if its timing parameters are distant enough from the boundaries of the predefined clusters. To show the effectiveness of AutoSec, we collected timing traces from a testbed composed of a set of real and virtual control units communicating over a CAN bus. We show that using our proposed AutoSec, compared to baseline methods, we can identify up to 21% less false positives and 18% less false negatives.

关键词： Anomaly Detection real-time Operating systems Timing Constraints DBSCAN Classifiers

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Interrupting real-time IoT Tasks: How Bad Can It Be to Connect Your Critical embedded System to the Internet? 39

Interrupting Real-Time IoT Tasks: How Bad Can It Be to Conne...

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39th ieee international Performance computing and Communications conference (IPCCC)

作者： Behnke, Ilja Pirl, Lukas thamsen, Lauritz Danicki, Robert Polze, Andreas Kao, Odej Tech Univ Berlin Berlin Germany Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781728198293

embedded systems have been used to control physical environments for decades. Usually, such use cases require low latencies between commands and actions as well as a high predictability of the expected worst-case delay. To achieve this on small, low-powered microcontrollers, real-time Operating systems (RTOSs) are used to manage the different tasks on these machines as deterministically as possible. However, with the advent of the Internet of things (IoT) in industrial applications, the same embedded systems are now equipped with networking capabilities, possibly endangering critical real-time systems through an open gate to interrupts. this paper presents our initial study of the impact network connections can have on real-time embedded systems. Specifically, we look at three aspects: the impact of network-generated interrupts, the overhead of the related networking tasks, and the feasibility of sharing computing resources between networking and real-time tasks. We conducted experiments on two setups: One treating NICs and drivers as black boxes and one simulating network interrupts on the machines. the preliminary results show that a critical task performance loss of up to 6.67% per received packet per second could be induced where lateness impacts of 1% per packet per second can be attributed exclusively to ISR-generated delays.

关键词： Internet of things real-time Interrupts Cyber Physical systems embedded systems

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Improved YOLOv8 Based Small Object Detection for Intelligent Robotic Arm in Complex Environments

Improved YOLOv8 Based Small Object Detection for Intelligent...

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Data Driven Control and Learning systems (DDCLS)

作者： Zhiqing Li Dinglinag Ren College of Information Science and Technology Beijing University of Chemical Technology Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Small objects in complex environments may be very close to the background, which brings great challenges to computer vision based intelligent robotic arms. To address these problems, an improved YOLOv8 based small object detection method is proposed in this paper. Firstly, the YOLOv8 prediction heads are reconstructed to improve the prediction performance of small objects. An adaptive multi-scale convolutional attention module (MSCA) is introduced in the backbone which leverages multi-scale features to apply spatial attention with element multiplication. Part of convolutional modules is replaced with GhostNet blocks. these GhostNet blocks generate numerous ghost feature maps with cost-effective linear transformations based on intrinsic feature maps. Finally, the coordinate regression loss function is also optimized with Wasserstein divergence. Experimental results show that the improved YOLOv8 achieves a 0.093 improvement in mean average precision (mAP) compared to vanilla YOLOv8. For practical deployment applications, a sophisticated spatially-aware intelligent robotic arm is proposed by combining the improved YOLOv8 and laser ranging. the practical application results demonstrate that the proposed method has high feasibility and stability.

关键词： Program processors embedded systems Robot kinematics Computational modeling Object detection Manipulators real-time systems

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Atlas: Ensuring Accuracy for Privacy-Preserving Federated IoT applications 25

Atlas: Ensuring Accuracy for Privacy-Preserving Federated Io...

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Proceedings of the ACM/ieee 16th international conference on Cyber-Physical systems (with CPS-IoT Week 2025)

作者： Jiechao Gao Mingyue Tang Wenpeng Wang Tushar Routh Bradford Campbell University of Virginia Charlottesville VA USA University of Illinois Urbana-Champaign Champaign IL USA

ISBN: (纸本)9798400714986

In smart Internet of things (IoT) applications, edge devices often collect and store limited data, which is insufficient for training modern deep learning models. Collaborative training methods like cloud computing and federated learning enable robust models for IoT applications, yet introduce data privacy concerns due to central data collection and model inversion attacks. Remedies such as differential privacy can bring data privacy protection but dramatically degrade the accuracy performance of IoT applications. To safeguard user data privacy while maintaining application quality, it is imperative to establish a framework capable of preserving user privacy without compromising accuracy *** this paper, we present Atlas, a private and accurate personalized federated local differential privacy (LDP) framework for IoT applications. We first design a layer-sharing mechanism called the layer importance mask to separate the local model into global and personalized layers. Second, we design a weighted LDP mechanism and add noise to the global layers before transmitting them to the federated learning framework for aggregation. third, we combine local personalized layers and aggregated global layers to perform IoT tasks. Our experiments on five real-world IoT application datasets and the CIFAR-10 dataset show that our privacy-preserving approach only sacrifices 2% to 6% of accuracy compared to the state-of-the-art non-privacy preserving FL frameworks among various IoT applications and outperforms the current LDP-based FL framework by 8% to 13%.

关键词： Federated Learning

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Seeding-Based Multi-Objective Evolutionary Algorithms for Multi-Cloud Composite applications Deployment 13

Seeding-Based Multi-Objective Evolutionary Algorithms for Mu...

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13th ieee international conference on Services computing (SCC) / 13th ieee international conference on Web Services (ICWS)

作者： Shi, Tao Ma, Hui Chen, Gang Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (纸本)9781728187891

there are an increasing number of enterprises deploying their application services to multi-cloud to benefit the advantages brought by cloud computing. the multi-cloud composite applications deployment problem (MCADP) aims to select proper cloud resources from multiple cloud providers at different locations to deploy applications with shared constituent services so as to optimize application performance and deployment cost. Multi-objective evolutionary algorithms (MOEAs) can be utilized to find a set of trade-off solutions for MCADP. During population initialization of MOEAs, seeding strategies can considerably improve the algorithms' performance. For example, the seeding-based MOEAs, AO-Seed and SO-Seed, introduce a pre-optimization phase to search for solutions to be embedded into the initial population of MOEAs. With the extra optimization overhead, however, the two seeding-based MOEAs can only identify one or a limited few solutions to MCADP utilized by MOEAs. To solve MCADP effectively and efficiently, we propose new seeding-based MOEAs in this paper. the approach can construct application-specific seeds according to problem domain knowledge and build a group of diverse and high-quality solutions for the initial population of MOEAs. Extensive experiments have been conducted on a real-world dataset. the results demonstrate that the proposed seeding-based MOEAs outperform SO-Seed and AO-Seed with less computation cost for MCADP.

关键词： Location composite application multi-objective evolutionary algorithm seeding strategy multi-cloud

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Photonic-aware Neural Networks for Packet Classification in beyond 5G Networks 13

Photonic-aware Neural Networks for Packet Classification in ...

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13th international conference on the Network of the Future, NoF 2022

作者： Paolini, Emilio Civerchia, Federico De Marinis, Lorenzo Valcarenghi, Luca Maggiani, Luca Andriolli, Nicola Scuola Superiore sant'Anna Pisa56124 Italy TeCIP Institute Departments of Excellence Robotics & AI Italy National Research Council of Italy CNR-IEIIT Pisa56122 Italy Sma-RTy Italiat SRL Carugate20061 Italy

ISBN: (数字)9781665472548

ISBN: (纸本)9781665472548

the benefits introduced by novel network technologies such as 5G and beyond, including low latency and support for billions of devices, have the potential to transform the lives of people. However, the features promised by these new technologies have also attracted malicious actors, with various motivations for attacking the network infrastructure, from cybercrime-based frauds to political goals. thus, to enable the full potential of the emerging network technologies, it is necessary to take into accounts these attacks and develop tailored countermeasures. One future direction in mitigating the risks of potential attacks is the automatic classification of malicious packets, with the possibility to drop them if classified in the attack category. Hence, in this context, we propose a solution based on Neural Networks (NNs) to automatically classify packets into two classes, i.e., benign and attack, directly in the Radio Access Network (RAN), specifically inspecting packets when they are relayed at the next generation eNB (gNB)-Central Unit (CU) level. Since NNs can be computationally intensive algorithms, potentially increasing the latency of the network, we decide to leverage Photonic-Aware Neural Network (PANN), photonic accelerators able to perform NN computations in the analog optical domain and with time-of-flight latency. We devised two different PANN architectures, considering different photonic constraints. the classification performance of the two architectures has been assessed on the CICIDS-2017 dataset and compared with electronic counterparts. Results proved that the F1-score loss due to underlying hardware constraints is negligible, paving the way for PANN applications in next generation networks. © 2022 ieee.

关键词： 5G mobile communication systems

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Performance Analysis of Open-Source Hypervisors for Automotive systems 27

Performance Analysis of Open-Source Hypervisors for Automoti...

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27th ieee international conference on Parallel and Distributed systems, ICPADS 2021

作者： Zhang, Zhengjun Liu, Yanqiang Chen, Jiangtao Qi, Zhengwei Zhang, Yifeng Liu, Huai Shanghai Jiao Tong University Shanghai China Shanghai E-propulsion Auto Technology Co. Ltd Shanghai China Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9781665408783

Nowadays, automotive products are intelligence intensive and thus inevitably handle multiple functionalities under the current high-speed networking environment. the embedded virtualization has high potentials in the automotive industry, thanks to its advantages in function integration, resource utilization, and security. the invention of ARM virtualization extensions has made it possible to run open-source hypervisors, such as Xen and KVM, for embedded applications. Nevertheless, there is little work to investigate the performance of these hypervisors on automotive platforms. this paper presents a detailed analysis of different types of open-source hypervisors that can be applied in the ARM platform. We carry out the virtualization performance experiment from the perspectives of CPU, memory, file I/O, and some OS operation performance on Xen and Jailhouse. A series of microbenchmark programs have been designed, specifically to evaluate the real-time performance of various hypervisors and the relevant overhead. Compared with Xen, Jailhouse has better latency performance, stable latency, and little interference jitter. the performance experiment results help us summarize the advantages and disadvantages of these hypervisors in automotive applications. © 2021 ieee.

关键词： Benchmarking

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Design of a Power-Efficient Static Random Access Memory Cell with Enhanced Stability for Internet of things applications

Design of a Power-Efficient Static Random Access Memory Cell...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Atharv Sharma Venushree Kahol Ashish Sachdeva Department of ECE Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

In today's tech-driven era, where the need for swift data processing is paramount, memory emerges as a critical component, playing a key role in ensuring the high speed functionality of modern devices across diverse applications. From IoT devices and embedded systems to networking applications, SRAM (static random access memory) plays a pivotal role across a multitude of domains, illustrating its versatile and widespread use in diverse technological fields. In all these domains, high-speed processing is essential, underscoring the critical need for efficiently storing and swiftly accessing digital information. In this work, a bit-line enabled IT SRAM cell (BLE10T), designed for low power consumption is presented, that also shows improved access time and Static Noise Margin (SNM). the efficacy of low power BLE10T SRAM cell is also compared with conventional 6T SRAM cell. Comparing the BLE10T SRAM cell with the conventional 6T SRAM cell shows that the low power BLE10T SRAM cell consumes $\boldsymbol{1.52}\times, \boldsymbol{1.05}\times$ and $\boldsymbol{2.35}\times$ times less power during hold, read and write operation. Moreover, it is $\boldsymbol{1.67}\times$ times faster during read operation and $\boldsymbol{1.15}\times$ times faster during write operation. the static noise margin during hold, read and write operation is improved by $\boldsymbol{1.01}\times \boldsymbol{1.94}\times$ and $\boldsymbol{1.05}\times$ respectively, compared to conventional 6T bit-cell. the BLE10T cell and conventional 6T cell are simulated at 45nm technology node using cadence virtuoso simulator.

关键词： Resistance Power demand Satellites Power system management Noise SRAM cells Stability analysis

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WRLqueue: A Lock-Free Queue For embedded real-time System

WRLqueue: A Lock-Free Queue For Embedded Real-Time System

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Qidi Yang Lei Tang Yanrong Guo Nailiang Kuang Sheng Zhong Hangzai Luo Department of Software Engineering Xi'an Microelectronics Technology Institute Xian China Department of Software Engineering Northwest University Xian China

Data synchronization across tasks and devices are crucial for any system supporting multi-task. the standard lock-based blocking mechanism in the aforementioned process may have unpredictable latency overhead thus is not suitable for real time applications, and is prone to deadlock. As a result, lock-free and wait-free algorithms with worst-case latency and progress assurance are crucial for real time cases. Existing lock-free algorithms need to frequently allocate new storage buffers during execution, and design a series of complex memory recovery strategies and help mechanisms. It results in a high worst-case latency, complicated algorithm design, and poor embedded device adaptability. this work proposes a new lock-free method WRLqueue (write to read lock-free queue) for multi-producer and single consumer situation, which considerably enhances the real-time performance guarantee. Experimental results show that the cyclomatic complexity is reduced by 85% compared with existing algorithms. the worst-case latency of enqueue operation decreases by 90% in the case of no competition and 60% in the case of competition.

关键词： Performance evaluation System recovery throughput Multitasking real-time systems Complexity theory Synchronization

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RITA: Efficient Memory Allocation Scheme for Containerized Parallel systems to Improve Data Processing Latency 13

RITA: Efficient Memory Allocation Scheme for Containerized P...

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ieee 13th international conference on Cloud computing (CLOUD)

作者： Jia, Danlin Bayati, Mahsa Lee, Ron Mi, Ningfang Northeastern Univ Boston MA 02115 USA Samsung Semicond San Jose CA USA

ISBN: (纸本)9781728187808

the emerging resource-sharing container-based virtualization is prevalent in IT, as it is a much lighter deployment in the cloud environment compared to VM-based virtualization. Distributed data-processing workloads executing in parallel take advantages of resource sharing, fast delivery, and excellent portability of containerization, but also suffer from resource competition and performance interference. Especially for memory virtualization, data-processing frameworks allocate physical memory (i.e., RAM) and swap to applications specified by users, without considering cache-characteristics and parallelism of applications, which induces performance degradation and significantly protracted latency which is worse given over-provisioning. We design an efficient memory allocation scheme (RITA) for containerized parallel systems to improve data processing latency. RITA monitors memory usage and cache characteristics of applications, and dynamically re-allocates memory resources. We implement RITA in a real-world system, which can easily migrate to other container-based virtualization environments. Our experimental results show that RITA provides remarkable latency improvement for memory intensive distributed data-processing workloads.

关键词： Containerization Distributed Data-processing Latency Memory Allocation

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