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Anomaly Detection in Microservices Architecture Using Graph neural networks

Anomaly Detection in Microservices Architecture Using Graph ...

引用

Euromicro conference on Parallel, distributed and network-Based processing

作者： Matthias Osswald Timothy Schönenberger Gokcan Cantali Wissem Soussi Gürkan Gür Zurich University of Applied Sciences (ZHAW) InIT Winterthur Switzerland Communication Systems Group CSG University of Zürich UZH Zürich Switzerland

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The rise of cloud computing has transformed application development and deployment, with Kubernetes emerging as a key platform for managing containerized applications. This paper explores the use of Graph neural networks (GNNs) to detect anomalies in Kubernetes clusters and proposes GNN-based Anomaly Detection in Kubernetes (GATAKU), which is instrumental in maintaining security and performance. Our work involves integrating Cilium for detailed network monitoring and data collection, setting up a Kubernetes cluster with k3s and Traefik, and simulating attack scenarios to generate realistic data. Data preprocessing and feature engineering prepare this data for the GNN training. We present the GATAKU model’s performance, highlighting metrics such as accuracy, precision, recall, and F 1 -score and compare it to baseline ML models, namely Support Vector Machine (SVM) and Random Forest (RF). Moreover, we discuss these findings and emerging challenges, including handling high-dimensional data, and explore practical implications for cybersecurity.

关键词： Support vector machines Training Cloud computing Analytical models Accuracy Computer architecture Graph neural networks Computer security Anomaly detection Tuning

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MDLoader: A Hybrid Model-Driven Data Loader for distributed Graph neural network Training 24

MDLoader: A Hybrid Model-Driven Data Loader for Distributed ...

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Proceedings of the SC '24 Workshops of the International conference on High Performance Computing, network, Storage, and Analysis

作者： Jonghyun Bae Jong Youl Choi Massimiliano Lupo Pasing Kshitij Merita Pei Zhang Khaled Z. Ibrahim Lawrence Berkeley National Laboratory USA Oak Ridge National Laboratory USA

ISBN: (纸本)9798350355543

Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning. In-memory distributed storage is preferred for its speed, enabling rapid, random, and frequent data access required by stochastic optimizers. Processes use one-sided or collective communication to fetch remote data, with optimal performance depending on (i) dataset characteristics, (ii) training scale, and (iii) interconnection network. Empirical analysis shows collective communication excels with larger mini-batch sizes and/or fewer processes, whereas one-sided communication outperforms at larger scales. We propose MDLoader, a hybrid in-memory data loader for distributed graph neural network training. MDLoader features a model-driven performance estimator that dynamically selects between one-sided and collective communication at the beginning of training using Tree of Parzen Estimators (TPE). Evaluations on NERSC Perlmutter and OLCF Summit show MDLoader outperforms single-backend loaders by up to 2.83× and predicts the suitable communication method with 96.3% (Perlmutter) and 94.3% (Summit) success rate.

关键词： Graph neural network

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A Robust distributed Recurrent neural network for Multi-Agent Consensus Control

A Robust Distributed Recurrent Neural Network for Multi-Agen...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yiwei Li Jiaxin Liu Lin Yang Yating Zhang Kunlin Liu Ge Zhou Liangze Yin Wei Dong National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recurrent neural networks (RNNs) are widely used in control system due to their dynamic capabilities. However, the control accuracy of RNN-based systems can be compromised by noise interference, and there has been little research on RNN-based control in disturbed multi-agent systems. To address this, we developed an enhanced distributed RNN (DRNN) structure and proposed a Novel DRNN-based Control Protocol (NDRNN-CP). This enhancement involves introducing a time-delay component, allowing the protocol to adaptively learn noise variation patterns. As a result, the NDRNN-CP effectively resists various periodic noise interferences and achieves more precise control of each agent. Additionally, our optimized activation function ensures that all agents reach consensus within a predefined time. To demonstrate the advantages of NDRNN-CP, we conducted extensive experiments that confirmed its significant improvements in noise signal resistance and convergence performance.

关键词： Resistance Protocols Recurrent neural networks Noise Resists Reliability Speech processing Multi-agent systems Convergence Resilience

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An Anomaly Detection Model for RISC-V in Automotive Applications: A Domain-Specific Accelerator Perspective

An Anomaly Detection Model for RISC-V in Automotive Applicat...

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Elio Vinciguerra Enrico Russo Maurizio Palesi Giuseppe Ascia Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Early anomaly detection in automotive systems is crucial for enhancing user safety and enabling timely corrective actions, thereby minimizing the risks associated with system malfunctions. This paper presents an approach for implementing Artificial Intelligence (AI)-based algorithms for anomaly detection in the automotive domain, leveraging the RISC-V architecture in conjunction with Domain-Specific Accelerators (DSAs). By exploiting the efficiency of DSAs, the proposed system aims to achieve faster anomaly detection compared to traditional processing methods. A detailed comparison is conducted between the performance of executing the AI-based anomaly detection algorithm on the RISC-V core versus offloading it to an optimized hardware accelerator tailored to the specific AI model. The goal of this work is to provide valuable insights into the potential of RISCV and DSAs to enhance AI-driven safety mechanisms, contributing to the development of more reliable automotive systems.

关键词： Program processors neural networks Real-time systems Safety Reliability Artificial intelligence Anomaly detection Hardware acceleration Optimization Monitoring

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Positional Differential Encoding for distributed Learning

Positional Differential Encoding for Distributed Learning

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Leah Woldemariam Anna Scaglione Department of Electrical and Computer Engineering Cornell University New York NY USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A growing amount of available data and computational power makes training neural networks over a network of devices, and distribution optimization in general, more realizable. As a consequence, efficient communication becomes more important and can often be the bottleneck of such algorithms. Traditional data compression techniques for these scenarios consider the correlation of the data values over time: although the transmitted data itself may not be sparse, the changes within the data are increasingly sparse as the algorithm converges. In this work, we propose an encoding scheme for discrete symbols that adapts a source encoder to consider the positional correlations through time. We provide a bound on the bit rate yielded by our scheme and verify our results through experiments.

关键词： Training Computer aided instruction Correlation Distance learning Bit rate neural networks Signal processing algorithms Symbols Encoding Speech processing

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A Noise-Based Approach Augmented with neural Cleanse and JPEG Compression to Counter Adversarial Attacks on Image Classification Systems

A Noise-Based Approach Augmented with Neural Cleanse and JPE...

引用

Euromicro conference on Parallel, distributed and network-Based processing

作者： Igor Kotenko Igor Saenko Oleg Lauta Nikita Vasiliev Vladimir Sadovnikov Laboratory of Computer Security Problems St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Saint-Petersburg Russia Department of Telecommunications and Information Security Admiral Makarov State University of Maritime and Inland Shipping Saint-Petersburg Russia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Adversarial attacks are now becoming quite a dangerous means of disrupting image processing systems that use machine learning methods for decision making. Therefore, developing effective countermeasures against adversarial attacks is becoming quite an important area of cybersecurity. The paper proposes a noise-based approach to countering adversarial attacks that is augmented with neural-cleanse and jpeg-compression technologies. The idea of the proposed approach is that adding noise distorts the effect of an adversarial attack, and neural cleaning and jpeg compression eliminate the consequences of such an effect. The paper examines the three most well-known types of adversarial attacks: Fast Gradient Sign Method, Zeroth Order Optimization and One Pixel Attack. These attacks manipulate input data, resulting in misclassification or incorrect predictions by exploiting high-frequency components that are undetectable to humans. The research was carried out on two datasets: MNIST-JPG and PC Parts Images. Two types of noise were used: Gaussian and Poisson. During the experiments, optimal parameters for these types of noise were found, ensuring maximum accuracy of image recognition after exposure to adversarial attacks.

关键词： Resistance Image recognition Image coding Image resolution Noise Transform coding Machine learning Distortion Optimization Image classification

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Artificial Intelligence techniques for space experiments

Artificial Intelligence techniques for space experiments

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Federica Cuna Maria Bossa Fabio Gargano Nicola Mario Mazziotta Pietro Betti Istituto Nazionale di Fisica Nucleare ICSC Bari Italy Istituto Nazionale di Fisica Nucleare ICSC Napoli Italy Istituto Nazionale di Fisica Nucleare Firenze Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The application of advanced Artificial Intelligence (AI) techniques in astroparticle experiments represents a groundbreaking advancement in data analysis and experimental design. As space missions become increasingly complex, integrating AI technologies is essential for optimizing their performance and enhancing their scientific outcomes. This study focuses on the use of Graph neural networks (GNNs) for tracking systems and Transformers for calorimeters in space-based experiments. A significant challenge for trackers in space experiments is the high-noise environment, characterized mainly by backscattering tracks. We will present a novel approach that employs GNNs for node-level tasks, specifically aimed at classifying backscattering hits versus signal hits and reconstructing tracks. The goal for the space calorimeter experiment is to distinguish between particle types, such as electrons and protons. By capturing the dependencies within these features, Transformers can achieve robust classifications, even when the data spans thousands of channels or dimensions. By addressing these challenges, we aim to enhance the accuracy and reliability of data interpretation in astroparticle physics through the application of advanced Artificial Intelligence techniques.

关键词： Training Protons Accuracy Space missions Clustering algorithms Transformers Graph neural networks Classification algorithms Artificial intelligence Backscatter

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Venus: A Versatile Deep neural network Accelerator Architecture Design for Multiple Applications 23

Venus: A Versatile Deep Neural Network Accelerator Architect...

引用

Proceedings of the 60th Annual ACM/IEEE Design Automation conference

作者： Jiaqi Yang Hao Zheng Ahmed Louri Electrical and Computer Engineering George Washington University Washington DC USA Electrical and Computer Engineering University of Central Florida Orlando Florida USA

ISBN: (纸本)9798350323481

Deep neural network (DNN) applications are pervasive. However as demands for these applications continue to increase, so is the challenges for designing flexible and scalable architectures for multi-application implementation. Such accelerators require innovative architecture with flexible network-on-Chips (NoCs), parallelism exploitation, and better on-chip memory organization to adequately support the diverse computation, memory, and communication needs. In this paper, we propose Venus, a versatile DNN accelerator design that can provide efficient communication and computation support for multi-applications. Venus is a tile-based architecture with a distributed buffer where each tile consists of an array of processing elements (PEs) and a portion of the distributed buffer. The other salient feature of Venus is a flexible network-on-Chip (NoC) that can dynamically adapt to the communication needs of various running applications thus maximizing data reuse, reducing DRAM accesses, and supporting multiple dataflows with an overall aim of better execution time and better energy efficiency. Simulation results show that our proposed Venus design outperforms state-of-art accelerators (NVDLA [1], ShiDianNao [2], Eyeriss [3], Planaria [4], Simba [5]).

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Benchmarking Quantum Convolutional neural networks for Signal Classification in Simulated Gamma-Ray Burst Detection

Benchmarking Quantum Convolutional Neural Networks for Signa...

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Farida Farsian Nicoló Parmiggiani Alessandro Rizzo Gabriele Panebianco Andrea Bulgarelli Francesco Schilliró Carlo Burigana Vincenzo Cardone Luca Cappelli Massimo Meneghetti Giuseppe Murante Giuseppe Sarracino Roberto Scaramella Vincenzo Testa Tiziana Trombetti OACT INAF Via S. Sofia 78 Catania Italy OAS INAF Via Pietro Gobetti 93/3 Bologna Italy IRA INAF Via Pietro Gobetti 101 Bologna Italy OAR INAF Via Frascati 33 Monteporzio Catone Italy OATs INAF Via Tiepolo 11 Trieste Italy OACN INAF Via Moiariello 16 Napoli Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

This study evaluates the use of Quantum Convolutional neural networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical observatory for very high-energy gamma-ray science. QCNNs, a quantum counterpart of classical Convolutional neural networks (CNNs), leverage quantum principles to process and analyze high-dimensional data efficiently. We implemented a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator. Several QCNN architectures were tested, employing different encoding methods such as Data Reuploading and Amplitude encoding. Key findings include that QCNNs achieved accuracy comparable to classical CNNs, often surpassing 90%, while using fewer parameters, potentially leading to more efficient models in terms of computational resources. A benchmark study further examined how hyperparameters like the number of qubits and encoding methods affected performance, with more qubits and advanced encoding methods generally enhancing accuracy but increasing complexity. QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision. The research is a pioneering effort in applying QCNNs to astrophysics, offering insights into their potential and limitations. This work sets the stage for future investigations to fully realize the advantages of QCNNs in astrophysical data analysis.

关键词： Observatories Accuracy Quantum advantage Gamma-ray bursts Qubit Benchmark testing Telescopes Encoding Convolutional neural networks Signal detection

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Machine Learning Supernovae’s Progenitor Characterization

Machine Learning Supernovae’s Progenitor Characterization

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Marco Grassia Stefano Pio Cosentino Maria Letizia Pumo Giuseppe Mangioni Dipartimento di Ingegneria Elettrica Elettronica e Informatica University of Catania Italy Dipartimento di Fisica e Astronomia University of Catania Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

In this work, we present a Deep Learning framework to predict the progenitor star’s characteristics of Supernovae (SNe) from their observed light curves. This task is crucial for astrophysics, as it can provide insights into the evolution of the star before the explosion and into the SN explosion mechanism. In fact, there is no direct mapping between the observed light curves and the progenitor’s characteristics, and the common techniques used to infer them are indirect, meaning that they rely on the comparison between the observed light curves and the light curves generated with some physical model simulation using the supposed progenitor’s characteristics as input. However, the physical models used to generate the light curves are not perfect, being either computationally expensive or based on simplifying assumptions. Here, we train a machine learning model on a dataset of light curves generated with a semi-analytical model — which is computationally efficient and accurate for the problem at hand — to predict the progenitor’s mass, radius, energy, and nickel mass from the observed light curves of SNe similar to SN 1987A. Our results show that the Deep Learning model effectively learns the complex mapping between the observed light curves and the progenitor’s characteristics with a low mean absolute percentage error, and we note that the proposed framework is general and can be applied to other types of SNe without significant modifications.

关键词： Deep learning Hands Astrophysics Computational modeling neural networks Stars Predictive models Explosions Nickel Computational efficiency

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