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SFTA: Spiking Neural networks Vulnerable to Spiking Feature Transferable Attack

SFTA: Spiking Neural Networks Vulnerable to Spiking Feature ...

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IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Xuanwei Lin Chen Dong Ximeng Liu College of Computer and Data Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University) Key Lab of Information Security of Network Systems (Fuzhou University)

Many recent works have shown that spiking neural networks (SNNs) are vulnerable to well-crafted adversarial attacks in the white-box setting, which may lead to misclassification of the trained model. However, the information about the model is in a black-box setting and is usually unknown to the attacker, e.g., the parameters and structure of the model. So it is more challenging to counter the attack in the black-box setting, and there is still little research on this aspect in SNNs. Therefore, this work describes a transfer-based black box adversarial attack against spike feature space representations, providing insights for robust spike features in SNNs models. The attack is explicitly designed to be transferable, and more robust feature gradients are obtained by accumulating the feature gradients of the intermediate layers. Then we can successfully attack the black-box model by suppressing robust positive features and enhancing negative features. Extensive experimental evaluation demonstrates the proposed method’s effectiveness and superior performance on static and neuromorphic datasets. i.e., The attack success is 89.72% and 90% on the static MNIST and CIFAR10 datasets, and 85.97% and 69.09% on the neuromorphic N-MNIST and CIFARO-DVS datasets.

关键词： Correlation Neuromorphics Neural networks Closed box Ubiquitous computing Optimization Glass box

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A Method for IoT Device Management and Traffic Scheduling in Distribution Station Area Based on distributed SDN Architecture

A Method for IoT Device Management and Traffic Scheduling in...

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2022 Asian conference on Frontiers of Power and Energy, ACFPE 2022

作者： Guanqiang, Lin Jinyong, Lei Quan, Xu Xiaoguang, Wang Lei, Yu Fukui, Li Jingnan, Lin Huizhou Power Supply Bureau of Guangdong Power Grid Corporation Gunagdong China Electric Power Research Institute Csg Gunagdong China

ISBN: (数字)9781665470841

ISBN: (纸本)9781665470841

Aiming at the increasing data transmission and computation-intensive analysis and decision-making in the construction of the distribution Internet of Things, an Internet of Things system in the distribution station area based on the distributed SDN architecture is constructed. Including distributed control module, data exchange module and adaptive access of IoT devices, different types of IoT devices can be associated with heterogeneous access points, and exchange information with switches by coordinating multiple controllers. In this paper, the controller transforms the mobile device management and access point assignment problem into a joint optimization problem and solves it using adaptive time windows as a tool. Finally, a deep reinforcement learning traffic scheduling algorithm is used to optimize the capacity of the access point and reduce the delay of data transmission. The simulation results show that the method can find the best access point within the tolerable delay time, realize the interconnection technology of the IoT gateway in the distribution station area, and ensure the stable operation of the business in the distribution station area. © 2022 IEEE.

关键词： Internet of things

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FAWS: Fault-Aware Weight Scheduler for DNN Computations in Heterogeneous and Faulty Hardware

FAWS: Fault-Aware Weight Scheduler for DNN Computations in H...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Shaswot Shresthamali Yuan He Masaaki Kondo Keio University

The idea of using inexact computation for overprovisioned DNNs (Deep Neural networks) to decrease power and la-tency at the cost of minor accuracy degradation has become very popular. However, there is still no general method to schedule DNN computations on a given hardware platform to effectively implement this idea without loss in computational efficiency. Most contemporary methods require specialized hardware, ex-tensive retraining and hardware-specific scheduling schemes. We present FAWS: Fault-Aware Weight Scheduler for scheduling DNN computations in heterogeneous and faulty hardware. Given a trained DNN model and a hardware fault profile, our scheduler is able to recover significant accuracy during inference even at high fault rates. FAWS schedules the computations such that the low priority ones are allocated to inexact hardware. This is achieved by shuffling (exchanging) the rows of the matrices. The best shuffling order for a given DNN model and hardware fault profile is determined using Genetic Algorithms (GA). We simulate bitwise errors on different model architectures and datasets with different types of fault profiles and observe that FAWS can recover up to 30% of classification accuracy even at high fault rates (which correspond to approximately 50 % power savings).

关键词： Degradation Deep learning Schedules Costs Processor scheduling Computational modeling Neural networks

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parallel Deployment of VNFs in Service Function Chain: Benefit or Not?

Parallel Deployment of VNFs in Service Function Chain: Benef...

引用

IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Kaiwen Ning Hao Wang Zhiheng Zhang Zhou Xu Xiaowei Shu School of Software Engineering Dalian University of Technology China

Network Function Virtualization (NFV) technology facilitates flexible and fast service provisioning by deploying network functions in wished positions and chaining order as Service Function Chain (SFC). Empirically, if the processing inside some Virtual Network Functions (VNFs) does not relate to each other, parallel chaining them can further improve the performance, such as reducing the end-to-end hops of SFCs. However, it is often ignored that parallel deploy VNFs will also introduce additional VNFs to replicate, distribute, and merge data. This will add hops and increase resource consumption. Will parallel deployment of VNFs in SFCs necessarily bring benefits? Regarding multiple parallel forms of an SFC, which one is the best? Further, how does the service process of the NFV network affect the parallel decisions of VNFs? In this paper, we design an NFV orchestrator, DP-NFVO, to maximize the revenue of NFV Service Providers and provide quantitative insights into the impact of parallel VNFs deploy-ment. Precisely, we first refine existing VNF parallelism rules and discuss the impact of VNF parallelism on latency and resource consumption, and analyze the optimal form of parallelism for SFCs. Second, we decompose the service request process in NFV networks into multiple stages and provide a joint model of VNF parallelism and deployment. Further, we design a VNF online deployment and parallelism decision policy for DP-NFVO based on Proximal Policy Optimization (PPO) to maximize revenue. Finally, we demonstrate the effectiveness and adaptability of DP-NFVO on parallel processing and the requests service process with extensive experiments.

关键词： Adaptation models Service function chaining parallel processing Network function virtualization Delays Optimization

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Blind Spectrum Sensing Scheme Based on Harmonic Mean of Eigenvalues of Sample Covariance Matrix

Blind Spectrum Sensing Scheme Based on Harmonic Mean of Eige...

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IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Tingting Liu Kejun Lei Zhewen Tan Xinxin Tian Yuhao Tan Xi Yang School of Communication and Electronic Engineering Jishou University Jishou P. R. China

Maximum eigenvalue-harmonic mean (MEHM) detection is a very promising algorithm for spectrum sensing in cognitive radio. However, the asymptotic analysis method currently used greatly limits the improvement of the detection performance of MEHM. For this, a novel maximum eigenvalue-harmonic mean (NMEHM) based spectrum sensing algorithm is proposed, using the results of the distribution of the limiting eigenvalues of the sample covariance matrix in random matrix theory. The proposed algorithm significantly improves the detection performance of the traditional MEHM algorithm and achieves better detection results than the classical eigenvalues based algorithms; meanwhile, the proposed algorithm does not require a priori knowledge of the primary user signal and wireless channel, which can effectively overcome the impact of noise uncertainty. Simulation results demonstrate the validity of the proposed algorithm.

关键词： Wireless sensor networks Uncertainty Limiting Simulation Reliability theory Ubiquitous computing Eigenvalues and eigenfunctions

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Attribute encryption based access control methods under airborne networks

Attribute encryption based access control methods under airb...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Zhijun Wu Zhenghang You Peng Wang School of Safety Science and Engineering Civil Aviation University of China TianJin China

In order to solve the problem of multiple information security risks and unclear intrusion paths under the airborne network, Firstly, this paper studies the cross-domain access of each security domain under the airborne network, Moreover, a access control method under the airborne network is proposed by combing attribute based encryption and the proposed method not only enables access control between different security domains under the airborne network, but also reduces the security risk of unauthorized access between different security domains under the airborne network. Finally, the simulation experiment proves that the implementation of access control function based on attribute encryption is feasible in the airborne network environment, and verifies the effectiveness of the attribute encryption-based technology in the airborne network and provides technical guarantee capability for the safe operation of air transportation, which can provide strong support for the wide application of attribute-based encryption technology.

关键词： Access control Data privacy Analytical models Atmospheric modeling Information security Air transportation Encryption

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Joint Task Partition and Computation Offloading for Latency-Sensitive Services in Mobile Edge networks

Joint Task Partition and Computation Offloading for Latency-...

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international conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Yujie Peng Xiaoqin Song Fang Liu Guoliang Xing Tiecheng Song National Mobile Communications Research Laboratory Southeast University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Information Engineering at The Chinese University of Hong Kong Shenzhen Research Institute Southeast University Shenzhen China

With the development of Internet of Things (IoT), wireless communication networks and Artificial Intelligence (AI), more and more real-time applications such as online games and autonomous driving have emerged. However, due to limited computing power and battery capacity, it has become increasingly difficult for local user devices to take on the full range of computing tasks under tight timing constraints. The emerging Mobile Edge computing (MEC) technology is widely considered to be an important technology for achieving ultra-low latency. However, most of the existing work is focused on non-splittable computation tasks. In fact, data partitioning-oriented applications can be split into multiple subtasks for parallel processing. In this paper, we study the partial computation offloading of multiple detachable tasks in MEC networks, focusing on minimizing the total user device latency in the multi-MEC multi-user scenarios. Considering the dynamic partitioning of tasks, we adopt the barrel theory to construct a linear system of equations to find the optimal solutions and propose an approach for distributed computation offloading based on numerical methods. The simulation results show that the proposed algorithm can reduce the average user device latency by 31 % compared with the binary offloading method.

关键词： Simulation Computational modeling Mathematical models Stability analysis Delays Numerical models Internet of Things

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A Service Placement Algorithm Based on Merkle Tree in MEC Systems Assisted by Digital Twin networks

A Service Placement Algorithm Based on Merkle Tree in MEC Sy...

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IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Chengan Dai Kun Yang Chunjian Deng School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China

This paper considers a digital twin network (DTN) assisted mobile edge computing (MEC) system. When wireless device (WD) users request services from MECs, we assume in the system there are a cloud server, storing all of service entities, and a lightweight digital twin network, providing the digital replicas of state information of all MECs instead of service entities. We aim to maximize the number of service requests served by the MECs, or, equivalently, to minimize the load of the cloud. This is formulated as a mixed-integer non-convex optimization problem. We propose a service placement algorithm based on hash-based data structure called Merkle tree to solve the problem. The introduction of candidate mode pruning effectively reduces the time complexity of the algorithm in iterations. Simulation results show that our proposed method has a better performance compared with the other benchmarks.

关键词： Wireless communication Multi-access edge computing Simulation Heuristic algorithms Benchmark testing Ubiquitous computing Digital twins

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An Edge Attribute-Wise Partitioning and distributed Processing of R-GCN Using GPUs

An Edge Attribute-Wise Partitioning and Distributed Processi...

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26th international conference on parallel and distributed computing (Euro-Par)

作者： Kibata, Tokio Tsukada, Mineto Matsutani, Hiroki Keio Univ 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9783030715939;9783030715922

GCN (Relational Graph Convolutional Network) is one of GNNs (Graph Neural networks). The model tries predicting latent information by considering directions and types of edges in graph-structured data, such as knowledge bases. The model builds weight matrices to each edge attribute. Thus, the size of the neural network increases linearly with the number of edge types. Although GPUs can be used for accelerating the R-GCN processing, there is a possibility that the size of weight matrices exceeds GPU device memory. To address this issue, in this paper, an edge attribute-wise partitioning is proposed for R-GCN. The proposed partitioning divides the model and graph data so that R-GCN can be accelerated by using multiple GPUs. Also, the proposed approach can be applied to sequential execution on a single GPU. Both the cases can accelerate the R-GCN processing with large graph data, where the original model cannot be fit into a device memory of a single GPU without partitioning. Experimental results demonstrate that our partitioning method accelerates R-GCN by up to 3.28 times using four GPUs compared to CPU execution for a dataset with more than 1.6 million nodes and 5 million edges. Also, the proposed approach can accelerate the execution even with a single GPU by 1.55 times compared to the CPU execution for a dataset with 0.8 million nodes and 2 million edges.

关键词： GPU R-GCN GNN Graph data Knowledge base

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CopGAT: Co-propagation Self-supervised Graph Attention Network

CopGAT: Co-propagation Self-supervised Graph Attention Netwo...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Baoming Zhang Ming Xu Mingcai Chen Mingyuan Chen Chongjun Wang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Graph Attention Network (GAT) is one of the state-of-the-art architectures for Graph Neural networks (GNNs). In this paper, we first propose Label Purity to explore the relationship between the graph attention and the node labels. By tracking the label purity of graph attention, we observe that graph attention suppresses message passing between inter-class nodes in the graph with homophily. Upon such basis, we further improve graph attention's capability to capture label information by designing a self-supervised graph attention loss. We propose a novel approach based on self-supervised graph attention loss, Co-propagation Self-supervised Graph Attention Network (CopGAT), which explicitly restrains message passing between inter-class nodes. The Co-propagation means propagating node representations in Euclidean space and pseudo labels in probability simplex via the shared attention matrix, combining Message Passing Neural networks and Label Propagation Algorithm under the graph attention framework. Constrained with the self-supervised graph attention loss generated by co-propagation, our CopGAT enhances the ability of GAT to attenuate the influence of structural noise. Furthermore, our self-supervised graph attention loss can be extended to other graph attention models in a plug-and-play manner. Experimental results demonstrate that our Cop GAT outperforms previous state-of-the-art methods on five benchmark datasets for the semi-supervised node classification task.

关键词： Message passing Benchmark testing Graph neural networks Task analysis

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