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Federated Learning-based Intrusion Detection on Non-IID Data 1

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22nd International conference on Algorithms and Architectures for parallel processing, ICA3PP 2022

作者： Liu, Yongfei Wu, Guangjun Zhang, Wenyuan Li, Jun Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

Intrusion detection is an effective means to deal with network attacks. Currently, the commonly used detection methods are based on machine learning. However, traditional machine learning-based methods are centralized architectures that require uploading data to cloud servers, which face serious latency and data security issues. Federated learning (FL) can collaboratively train a machine learning model with good performance while the data is kept locally on the client, which can effectively make up for the shortcomings of the centralized architecture. Most of the current research on using FL methods in machine learning-based intrusion detection ideally consider the data to be independent and identically distributed (IID), which doesn’t conform to real scenarios. In the real world, due to the different environment of the client, the types of attacks contained in the data owned by each client may be different. Therefore, we study the effects of various non-independent and identically distribution (non-IID) settings on FL in detail and give specific partitioning methods. In addition, we also propose a FL data rebalancing method based on auxiliary classifier generative adversarial networks (ACGAN), which is experimentally validated on the UNSW-NB15 dataset. Experiments show that the proposed data augmentation method can well improve the impact of non-IID data on FL. © 2023, Springer Nature Switzerland AG.

关键词： Intrusion detection

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8th Latin American High Performance Computing conference, CARLA 2021

8th Latin American High Performance Computing Conference, CA...

引用

8th Latin American High Performance Computing conference, CARLA 2021

ISBN: (纸本)9783031042089

The proceedings contain 18 papers. The special focus in this conference is on Latin American High Performance Computing. The topics include: Accelerating Smart City Simulations;distributed Artificial Intelligent Model Training and Evaluation;large-Scale distributed Deep Learning: A Study of Mechanisms and Trade-Offs with PyTorch;wind Prediction Using Deep Learning and High Performance Computing;an Analysis of Neural Architecture Search and Hyper Parameter Optimization Methods;Solving the Heat Transfer Equation by a Finite Difference Method Using Multi-dimensional Arrays in CUDA as in Standard C;High-Throughput of Measure-Preserving Integrators Derived from the Liouville Operator for Molecular Dynamics Simulations on GPUs;an Efficient parallel Model for Coupled Open-Porous Medium Problem Applied to Grain Drying processing;Energy Consumption Studies of WRF Executions with the LIMITLESS Monitor;Improving Performance of Long Short-Term Memory networks for Sentiment Analysis Using Multicore and GPU Architectures;a Methodology for Evaluating the Energy Efficiency of Post-Moore Architectures;Understanding COVID-19 Epidemic in Costa Rica Through network-based Modeling;an Efficient Vectorized Auction Algorithm for Many-Core and Multicore Architectures;Green Energy HPC Data Centers to Improve processing Cost Efficiency;DICE: Generic Data Abstraction for Enhancing the Convergence of HPC and Big Data;a Comparative Study of Consensus Algorithms for distributed Systems.

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Research on parallel LSTM Algorithm based on Spark 21

Research on Parallel LSTM Algorithm Based on Spark

引用

IEEE/ACIS 21st International Fall Virtual conference on Computer and Information Science (ICIS Fall)

作者： Zhao Yangyang Niu Wei Wang Meinan Xian Aeronaut Comp Tech Res Inst Xian Peoples R China

ISBN: (纸本)9781728176796

Aiming at the problems of large amount of data collected by airborne sensors, lack of data association, and low processing efficiency, this paper proposes a parallel LSTM algorithm model suitable for Spark platform. First, use the Spark platform to complete the traversal scan operation in the memory RDD of all nodes in the distributed cluster, and combine the directed acyclic graph to create a Pipeline pipeline to implement a parallel computing framework. An algorithm model to optimize the parameters of LSTM neural network is proposed, and load balancing processing method is introduced to realize that all nodes of the distributed system can share the computing tasks in a balanced manner. The experimental results show that compared to the stand-alone case, the parallelized LSTM algorithm improves the efficiency. The prediction efficiency of the LSTM algorithm model after load balancing processing is higher, which shows that the distribution of traversal tasks of each node is more balanced and the degree of parallelization is higher.

关键词： parallel computing LSTM load balancing processing

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2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication conference, IEMCON 2022

2022 IEEE 13th Annual Information Technology, Electronics an...

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13th IEEE Annual Information Technology, Electronics and Mobile Communication conference, IEMCON 2022

ISBN: (纸本)9781665463164

The proceedings contain 93 papers. The topics discussed include: edge-cloud synergy: unleashing the potential of parallel processing for big data analytics;access delegation framework for private decentralized patient health records sharing system based on blockchain;fair and robust bio-inspired resource sharing in distributed dynamic spectrum access networks;trust development for blockchain interoperability using self-sovereign identity integration;abnormality detection in network traffic by classification and graph data analysis;an automated system to calculate marks from answer scripts;the problem with regular multiple byte block boundaries in encryption;on the identification of isomorphic graphs for graph neural network using multi-graph approach;a hybrid method based on machine learning to predict the stock prices in Bangladesh;cataract detection and grading using ensemble neural networks and transfer learning;and a tool for the dynamic allocation of multiple marine activities.

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Accelerating the performance of data analytics using network-centric processing 21

Accelerating the performance of data analytics using network...

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15th ACM International conference on distributed and Event-based Systems, DEBS 2021

作者： Boughzala, Bochra Koldehofe, Boris University of Groningen Bernoulli Institute Groningen Netherlands

ISBN: (纸本)9781450385558

distributed execution of real-time data analytics such as event stream processing is the key to scalability, performance and reliable detection of situation changes. Although real-time analytics is highly I/O centric, existing methods supporting the efficient execution of data analytics functions mostly rely on traditional compute models that are available in data centers, e.g., CPU or GPU based processing models, but treat the network mainly as a blackbox. However, with recent advance in software-defined networking (SDN) and the standardization of packet processing pipeline, data analytics functions can be offloaded to programmable switches and benefit from hardware acceleration in an easier and more flexible way than a decade ago. In this paper we focus on the potential of in-network processing to enhance the performance of the overall real-time data analytics application. We aim to contribute to an (i) understanding on how in-network processing can accelerate real-time data analytics and (ii) assess what models of in-network computing can accelerate which event processing functions considering the limitations of network models compared to traditional compute models. We motivate the potential and illustrate the research problems in the context of load balancing which is an important concept in the data-parallel execution of event processing systems. © 2021 ACM.

关键词： Data Analytics

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Application of Various Novel Computer Image processing (CIP) Algorithms in Digital Media Art Images

Application of Various Novel Computer Image Processing (CIP)...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Xian Du Shanghai Institute of Visual Arts Shanghai China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm.

关键词： Training Image coding Art Image analysis Translation Image edge detection Digital images Semantics Media Feature extraction

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Efficiently parallelizable Strassen-based Multiplication of a Matrix by its Transpose 21

Efficiently Parallelizable Strassen-Based Multiplication of ...

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50th International conference on parallel processing (ICPP)

作者： Arrigoni, Viviana Maggioli, Filippo Massini, Annalisa Rodola, Emanuele Sapienza Univ Rome Dept Comp Sci Rome Italy

ISBN: (纸本)9781450390682

The multiplication of a matrix by its transpose, AT A, appears as an intermediate operation in the solution of a wide set of problems. In this paper, we propose a new cache-oblivious algorithm (AtA) for computing this product, based upon the classical Strassen algorithm as a sub-routine. In particular, we decrease the computational cost to 2/3 the time required by Strassen's algorithm, amounting to 14 3 nlog2 7 floating point operations. AtA works for generic rectangular matrices, and exploits the peculiar symmetry of the resulting product matrix for saving memory. In addition, we provide an extensive implementation study of AtA in a shared memory system, and extend its applicability to a distributed environment. To support our findings, we compare our algorithm with state-of-the-art solutions specialized in the computation of AT A. Our experiments highlight good scalability with respect to both the matrix size and the number of involved processes, as well as favorable performance for both the parallel paradigms and the sequential implementation, when compared with other methods in the literature.

关键词： Matrix Multiplication Cache-oblivious algorithm Fast Linear Algebra MPI OpenMP C/C plus

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network Traffic Classification Using distributed ML-based Data parallelization Approach

Network Traffic Classification Using Distributed ML-Based Da...

引用

Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Muhammad Musa K Abid Hussain Muhammad Jaseemuddin Rasha Kashef Department of ECE Toronto Metropolitan University Toronto Canada

The large datasets related to network traffic flow classification in the internet benefit machine learning (ML) and deep learning (DL) models for more accurate classification, which is used in many applications such as in detecting traffic anomaly for prevention of potential cyber-attacks. Data parallelization allows for faster training times on large datasets as shown in our results, and it is also beneficial in the cloud-edge environment by allowing efficient distribution of computation and data across multiple nodes. We deployed Convolutional Neural network (CNN), Long Short-Term Memory (LSTM), advanced hybrid Convolutional LSTM (ConvLSTM), Convolutional GRU (ConvGRU), and XGBoost algorithm using Data parallelization approach. The experimental setup was implemented in the cloud and parallel training was executed using Nvidia Tesla Graphics processing Units (GPUs). Lastly comparison of the performance metric results is presented between the non-parallel centralized (single node) and data parallel distributed approaches.

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Evaluating a New Attention Framework based on Matrix Blocking for Attention Models on FPGAs 34

Evaluating a New Attention Framework Based on Matrix Blockin...

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34th IEEE International conference on Tools with Artificial Intelligence (ICTAI)

作者： Liu, Xiaohang Jiang, Jingfei Xu, Jinwei Gao, Lei Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9798350397444

The attention mechanism has recently shown superior performance in natural language processing and computer vision tasks. But its complex dataflow and large-scale matrix calculation with huge computing and memory overhead pose a great challenge for the design of hardware accelerators. And previous solutions that benefited from matrix partitioning are bounded by the softmax function. In this paper, we propose a new attention framework that can dramatically improve the performance of attention model inference for long sequence tasks on FPGAs. We design a novel accelerator architecture that employs two systolic arrays and a ping-pong structure to accelerate attention calculation. Meanwhile, we propose an analytical model to predict resource usage and performance, which guides a fast design space exploration. Experiments using the state-of-the-art BERT demonstrate the design achieves 4.61 and 1.24x improvement in speed and energy efficiency compared to CPU and GPU on the Xilinx XCZU11EG platform.

关键词： attention mechanism accelerator architecture softmax matrix blocking systolic array

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A Deep Learning Approach based on Continuous Wavelet Transform Towards Fall Detection 17th

A Deep Learning Approach Based on Continuous Wavelet Transfo...

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17th International conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Chen, Yingwen Wei, Yuting Pang, Deming Xue, Guangtao Natl Univ Def Technol Changsha 410015 Peoples R China Shanghai Jiao Tong Univ Shanghai 200030 Peoples R China

ISBN: (纸本)9783031192135;9783031192142

In this paper, we investigate device-free fall detection based on wireless channel state information (CSI). Here, we mainly propose a method that uses continuous wavelet transform (CWT) to generate images and then uses transform learning of convolutional networks for classification. In addition, we add a wavelet scattering network to automatically extract features and classify them using a long and short-term memory network (LSTM), which can increase the interpretability and reduce the computational complexity of the system. After applying these methods to wireless sensing technology, both methods have a higher accuracy rate. The first method can cope with the problem of degraded sensing performance when the environment is not exactly the same, and the second method has more stable sensing performance.

关键词： Intelligent wireless sensing Fall detection Continuous wavelet transform Deep learning

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