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检索条件"任意字段=Distributed Memory Computing Conference"

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distributed Denial-of-Service (DDoS) Detection Using Multitask Learning Based on Deep Learning

Distributed Denial-of-Service (DDoS) Detection Using Multita...

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International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Muhammad Fauzan Abyandani Parman Sukarno Aulia Arif Wardana School of Computing Telkom University Bandung Indonesia Faculty of ICT Wrocław University of Science and Technology Wrocław Poland

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

distributed Denial of Service (DDOS) is one of the most significant threats among a wide variety of threats that can attack increasingly vulnerable computer networks. Traditional detection methods often fail to effectively manage the complexity of modern attack scales because traditional methods usually rely on monitoring the traffic volume and identifying spikes as an attack. This approach causes ineffective, inaccuracy, and lack of scalability. To address these challenges, this research aims to develop an advanced and innovative approach to detect and classify DDOS attacks. We use Multitask Learning (MTL) combined with Deep Learning (DL) using three DL models: Multi-Layer Perceptron (MLP), Long Short-Term memory (LSTM), and Convolutional Neural Networks (CNN). Using the NF-CSE-CIC-IDS2018-V2 and NF-BoT-IoT-V2 datasets, our methodol-ogy includes data preprocessing, feature normalization, and adjustment to a specific model such as changing the input. The evaluation shows that MTL-CNN model achieves perfect scores across multiple metrics: 100% accuracy, 100% precision, 100% recall, and 100% F1-score, as well as an execution time of 32.77 seconds. Although there is an increase in the time metric because of MTL compared to Single Task Learning(STL), MTL combined with the DL algorithm allows the model to learn faster with shared representation. This allows faster learning over time using knowledge learned from both datasets, resulting in better generalization and stronger attack detection.

关键词： Measurement Deep learning Machine learning algorithms Accuracy Scalability Prediction algorithms Convolutional neural networks Reliability Computer crime Long short term memory

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Scheduling Big Machine Learning Tasks on Clusters of Heterogeneous Edge Devices

Scheduling Big Machine Learning Tasks on Clusters of Heterog...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Saumya Mathkar Shreya Aiyer Yashovardhan Bapat Pinki Pinki Arnab K. Paul Vinayak Naik Department of CSIS BITS Pilani Goa India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Edge computing has transformed machine learning by using computing closer to the data sources, thereby reducing latency. The ever-increasing volume of data has necessitated forming clusters of edge devices, possibly with heterogeneous capabilities. Managing heterogeneous resources such as computation and memory remains challenging. Given the capabilities of the edge devices, we need a simple technique suitable for an Edge computing *** introduce a scheduling mechanism that leverages Integer Linear Programming (ILP) to optimize the overall computation time of ML-based tasks. We implement our scheduling mechanism that efficiently allocates resources, ensuring tasks are executed parallel across the cores of edge devices in a cluster by minimizing computation time. For tasks large enough to fit on any core, we leverage distributed learning to train the model in pieces and later combine them. We employ our ILP-based scheduler for efficient task allocation and compare its performance with a Greedy, simple approach based on best-fit technique. We evaluate our approach on three cases of sets of tasks on the spectrum of uniformity and size. Our results demonstrate two times the speed gain for our ILP-based approach over Greedy for the category with the least uniformity and large size.

关键词： Performance evaluation Computer aided instruction Machine learning algorithms Processor scheduling Distance learning Soft sensors Machine learning Integer linear programming Resource management Edge computing

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Intelligent Image Captioning with InceptionV3, LSTM, and PySpark Integration

Intelligent Image Captioning with InceptionV3, LSTM, and PyS...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), International conference

作者： Thanu Kurian Jimsha K Mathew Syam Dev R S Anand P K Vignesh A Rakshith B S Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies such as PySpark, LSTM, and InceptionV3 to develop an effective image captioning system. We harness the power of PySpark, a distributed computing framework, to efficiently process large-scale image datasets and extract high-level features from images using the InceptionV3 convolutional neural network (CNN) model. These features capture the semantic information present in the images and serve as input to the caption generation model. The caption generation model utilizes LSTM neural network as the decoder component. LSTM is well-suited for sequential data processing and is capable of generating coherent and contextually relevant captions based on the extracted image features. The InceptionV3 model acts as the encoder, extracting meaningful visual features from input images, while the LSTM decoder generates captions by decoding these features into natural language descriptions. This multimodal approach enables the model to understand and describe the content of images accurately. Through experimentation and evaluation on diverse image datasets, our proposed system demonstrates promising results in generating accurate and human-like captions for a wide range of images. By integrating PySpark, LSTM, and InceptionV3, our image captioning system achieves state-of-the-art performance, highlighting the effectiveness of leveraging advanced technologies in solving complex AI tasks.

关键词： Visualization Electric potential Accuracy Computational modeling Feature extraction Decoding Convolutional neural networks Data mining Artificial intelligence Long short term memory

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Advanced Machine Learning Approach for Cyber Attack Detection in Cloud computing

Advanced Machine Learning Approach for Cyber Attack Detectio...

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2025 International conference on Intelligent Control, computing and Communications, IC3 2025

作者： Yadav, Shashi Bhushan Singh Shelke, Nitin Arvind Singh, Jagendra Yasmeen Pandey, Deependra Himanshi Bbd Northern India Institute of Technology Department Computer Science & Engineering Lucknow India School of Computer Science Engineering & Technology Bennett University Greater Noida India Cvr College of Engineering Department of CSE-DS Hyderabad India Amity School of Engineering and Technology Amity University Department of Ece Lucknow India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India

ISBN: (纸本)9798331527495

Cloud computing has become the most important part of modern IT infrastructure, which is scalable and flexible. However, with such wide-scale adoption, new vulnerabilities have been introduced into the system, making it a favorite target for cyber-attacks, especially distributed Denial-of-Service (DDoS) and Man-in-the-Cloud (MitC) attacks. Security measures in cloud environments are more than ever required to be effective. This study introduces a state-of-the-art approach using machine learning techniques to identify and prevent such attacks;the four machine learning algorithms that have been used here are Support Vector Machine, Artificial Neural Network, Recurrent Neural Network, and k-Nearest Neighbors. A dataset of 3,200 simulated records has been used for the purpose of training and testing;70% of data were used for training purposes, and 30% of data were used for testing purposes. The models were rated based on precision, running time, and memory usage. The results highlighted that SVM was the strongest model and had an accuracy of 97.86%, while ANN was about 93.4% followed by RNN which was about 89.45% and KNN at 86.7%. This would mean that among the above models, the SVM is the most efficient with respect to real-time attack detection as it is in high precision and has efficiency in resource use. The proposed approach can be very well applied to real-time cloud security systems, offering a robust solution in identifying and mitigating cyber threats in dynamic cloud environments, ensuring enhanced protection against evolving attack vectors. © 2025 IEEE.

关键词： Cloud computing Cyber-attack detection DDoS Machine learning Man-in-the-Cloud

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Advanced Machine Learning Approach for Cyber Attack Detection in Cloud computing

Advanced Machine Learning Approach for Cyber Attack Detectio...

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Intelligent Control, computing and Communications (IC3), International conference on

作者： Shashi Bhushan Singh Yadav Nitin Arvind Shelke Jagendra Singh Yasmeen Deependra Pandey Himanshi Department Computer Science & Engineering BBD Northern India Institute of Technology Lucknow India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE-DS CVR College of Engineering Hyderabad India Department of ECE Amity School of Engineering and Technology Amity University Lucknow India Department of Information Technology Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Cloud computing has become the most important part of modern IT infrastructure, which is scalable and flexible. However, with such wide-scale adoption, new vulnerabilities have been introduced into the system, making it a favorite target for cyber-attacks, especially distributed Denial-of-Service (DDoS) and Man-in-the-Cloud (MitC) attacks. Security measures in cloud environments are more than ever required to be effective. This study introduces a state-of-the-art approach using machine learning techniques to identify and prevent such attacks; the four machine learning algorithms that have been used here are Support Vector Machine, Artificial Neural Network, Recurrent Neural Network, and k-Nearest Neighbors. A dataset of 3,200 simulated records has been used for the purpose of training and testing; 70% of data were used for training purposes, and 30% of data were used for testing purposes. The models were rated based on precision, running time, and memory usage. The results highlighted that SVM was the strongest model and had an accuracy of 97.86%, while ANN was about 93.4% followed by RNN which was about 89.45% and KNN at 86.7%. This would mean that among the above models, the SVM is the most efficient with respect to real-time attack detection as it is in high precision and has efficiency in resource use. The proposed approach can be very well applied to real-time cloud security systems, offering a robust solution in identifying and mitigating cyber threats in dynamic cloud environments, ensuring enhanced protection against evolving attack vectors.

关键词： Support vector machines Cloud computing Accuracy Recurrent neural networks Computational modeling Machine learning Artificial neural networks Nearest neighbor methods Real-time systems Cyberattack

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37.2 A 2-Dimensional mm-Scale Network-on-Textiles (kNOTs) for Wearable computing with Direct Die-to-Yarn Integration of 0.6×2.15mm2 SoC and bySPI Chiplets

37.2 A 2-Dimensional mm-Scale Network-on-Textiles (kNOTs) fo...

引用

IEEE International conference on Solid-State Circuits (ISSCC)

作者： Anjali Agrawal Zhenghong Chen Braden E. Desman Jinhua Wang Akiyoshi Tanaka Fahim Foysal Charlie D. Hess Will Farrell Jim Owens Daniel S. Truesdell Benton H. Calhoun University of Virginia Charlottesville VA Nautilus Defense LLC Pawtucket RI

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

This paper proposes a scalable, 2-dimensional network-on-textiles (kNOT) comprised of systems-on-chip (SoCs) and “bySPI” networking chiplets that are jointly capable of supporting heterogeneous programming, multiple sensing modalities, and a distributed memory system. Emerging e-textiles must retain the flexibility and comfort of their host garment to be viable in wearable applications for healthcare, virtual reality, and sports [1]–[4]. Prior integration efforts have demonstrated textile computing by weaving flexible filament circuits [5]–[7], embroidering conductive yarns [8], and fabricating electronic fibers [9]–[10]. Still, these works suffer from a combination of bulky, rigid components, high cost, or 1-dimensionality (Fig. 37.2.1). To preserve the textile's look and feel, SoCs for fabric integration must also be highly miniaturized, feature minimal area-hungry IO interfaces, be easily programmable, and be fully integrable. The e-textile system in [11] integrated a health sensing chip onto a planar fashionable circuit board, yet the board (25×25mm 2 ) is 40× larger than the chip itself. The System-in-Fiber from [12] is fully autonomous but has many IO pads, is limited to 1D networks, and requires an interposer (4.7×3.7mm 2 ) that is 3.8× larger than the die. Most recently, a battery-less e-textile system in [13] integrates cm-scale harvesting tiles and an inductor onto a shirt, but it lacks an integrated SoC. Many wearable applications also demand substantial on-garment storage, yet large memories are unsuited for comfortable textile integration due to their sizable footprint. Our kNOT solution addresses this by replacing monolithic memory units with a distributed set of smaller memories. However, networking these chiplets via existing bus standards incurs significant area and power penalties [14]. Existing Body Area Networks highlight the potential of multi-chip solutions, but they lack seamless textile integration [15]–[17]. Hence, to truly realize a fabric

关键词： Chiplets Wires Integrated circuit interconnections Virtual reality Fabrics Weaving Sensors Synchronization Yarn Wearable devices

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Optimizing Stateful Microservice Migration in Kubernetes with MS2M and Forensic Checkpointing

Optimizing Stateful Microservice Migration in Kubernetes wit...

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International conference on Intelligence in Next Generation Networks (ICIN)

作者： Hai Dinh-Tuan Jialun Jiang Service-Centric Networking Technische Universität Berlin Berlin Germany Technische Universität Berlin Berlin Germany

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

The widespread adoption of microservices architecture in modern software systems has emphasized the need for efficient management of distributed services. While stateless mi-croservices enable straightforward migration, stateful microser-vices introduce added complexity due to the need to preserve in-memory state during migration. However, most container orchestrators, including Kubernetes, lack native support for live stateful service migration. This paper proposes an optimized migration scheme for stateful services in Kubernetes by integrating the Message-based Stateful Microservice Migration (MS2M) framework with Kubernetes' Forensic Container Checkpointing (FCC) feature. Key enhancements include support for migrating StatefulSet-managed Pods and the introduction of a Threshold-Based Cutoff Mechanism to handle high incoming message rates. Evaluation results demonstrate that MS2M for individual Pods reduces downtime by 96.986% compared to cold migration methods, while the StatefulSet approach provides greater flexibility in managing stateful services. These insights provide practical strategies for optimizing stateful microservice migration in cloud-native environments.

关键词： Checkpointing Cloud computing Technological innovation Forensics Microservice architectures FCC Computer architecture Containers Software systems Complexity theory

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Preparing for HPC on RISC-V: Examining Vectorization and distributed Performance of an Astrophysics Application with HPX and Kokkos 24

Preparing for HPC on RISC-V: Examining Vectorization and Dis...

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

作者： Patrick Diehl Panagiotis Syskakis Gregor Daiß Steven R. Brandt Alireza Kheirkhahan Srinivas Yadav Singanaboina Dominic Marcello Chris Taylor John Leidel Hartmut Kaiser Applied Computer Science (CCS-7) Los Alamos National Laboratory Los Alamos NM U.S.A. LSU Center for Computation & Technology Louisiana State University Baton Rouge LA U.S.A. IPVS University of Stuttgart Stuttgart Stuttgart Germany Tactical Computing Labs 1001 Pecan St. Lindsay TX U.S.A. Department of Physics and Astronomy Louisiana State University Baton Rouge LA U.S.A.

ISBN: (纸本)9798350355543

In recent years, interest in RISC-V computing architectures has moved from academic to mainstream, especially in the field of High Performance computing where energy limitations are increasingly a concern. As of this year, the first single board RISC-V CPUs implementing the finalized ratified vector specification are being released. The RISC-V vector specification follows in the tradition of vector processors found in the CDC STAR-100, the Cray-1, the Convex C-Series, and the NEC SX machines and accelerators. The family of vector processors offers support for variable-length array processing as opposed to the fixed-length processing functionality offered by SIMD. Vector processors offer opportunities to perform vector-chaining which allows temporary results to be used without the need to resolve memory *** this work, we use the Octo-Tiger multi-physics, multi-scale, 3D adaptive mesh refinement astrophysics application to study these early RISC-V chips with vector machine support. We report on our experience in porting this modern C++ code (which is built upon several open-source libraries such as HPX and Kokkos) to RISC-V. In addition, we show the impact of the RISC-V Vector extension on a RISC-V single board computer by implementing the std: experimental:simd interface and integrating it with our code. We also compare the application's performance, scalability, and power consumption on desktop-grade RISC-V computer to an A64FX *** results presented in this paper are part of a longer-term evaluation of RISC-V's viability for HPC applications.

关键词： HPX

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Real-Time Analytics for Big Data in Smart City Applications: Transforming Urban Environments with Data-Driven Insights

Real-Time Analytics for Big Data in Smart City Applications:...

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Automation and Computation (AUTOCOM), International conference on

作者： Avinash Khanderi Gnanajeyaraman Rajaram Ramy Riad Hussein Prashant Johri T. Mohanraj M. Balamurugan Walmart Inc. Senior Data Engineer Bentonville Arkansas Department of CSE SIMATS Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Department of EEE Excel Engineering College Komarapalayam India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

This paper offers an overview of real-time analytics for smart cities proposing a framework for real-time data processing to improve the urban landscape. In the proposed system, large-scale data-streams can be processed using advanced machine learning algorithms, edge computing, and distributed data storage. Analyzing the outcomes of the evaluation procedure evidences reportable performance enhancements in the areas of smart city applications. Average traffic speed in transportation systems in the same period rose by 60%, concurrency and response to incidents likewise cut down by 40% and 53% respectively. The energy management systems responded with a 16% decrease in maximum energy load and the waste reduction was by 50%, while the costs savings was by 25% increase. Public safety applications alone realised a 60% decrease in the time taken to detect a crime and a 28.6% increase in efficiency on disaster resources with an overall spike in public satisfaction of 25.7%. Such findings improve the understanding of how this framework can increase urban efficiency, sustainability, and quality of life, which makes the solution usable in future smart city projects.

关键词： Waste reduction Energy consumption Costs Smart cities Disasters memory Big Data Real-time systems Energy management systems Vehicle dynamics

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A Patient-Centric Blockchain-Based Framework with Layer-2 Integration for Secure and Scalable Healthcare Data Management

A Patient-Centric Blockchain-Based Framework with Layer-2 In...

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International Symposium on Advanced computing and Communication (ISACC)

作者： Wajda Tarannum Shafiqul Abidin Department of Computer Science Aligarh Muslim University Aligarh India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Today, personal data is vulnerable in the digital era, especially in the medical sector, where healthcare data is sensitive. Yet, it plays a significant role in deciding the health conditions of patients, doctors, and researchers. Healthcare data is associated with patients' history, adding more value and utility to patients, doctors, and Researchers. These data are usually stored in centralized databases, creating vulnerabilities and leading to cyber-attacks. Furthermore, some healthcare providers rely on third-party contractors for data storage, which can be expensive and unreliable since vendors use diverse security procedures. Ensuring the secure protection of data from cyber threats and breaches is vital. This study aims to design a framework that manages large volumes of data from numerous sources like laboratories and hospitals. As we know, the volume of healthcare data is persistently growing. To address these issues, we propose a blockchain-based Patient-Centric framework with layer-2 solutions for healthcare data management that enhances security, privacy, and transparency in handling sensitive medical information. The concept integrates blockchain technology and the Interplanetary File System (IPFS), a distributed cloud storage system. We created smart contracts in Solidity to promote safe interactions between patients and healthcare professionals, and a React application communicates with them using MetaMask. Layer-2 technology decreases congestion, allowing the system to process a wide range of transactions more effectively while maintaining performance and security. Furthermore, by keeping cryptographic proofs on-chain and huge files off-chain, our strategy improves data security and privacy while lowering storage costs.

关键词： Access control Cloud computing Costs Scalability Smart contracts memory Medical services Blockchains InterPlanetary File System Protection

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