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distributed Machine Learning Based Intrusion Detection in IoT 4

Distributed Machine Learning Based Intrusion Detection in Io...

引用

4th international conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Meena, M. Sathish Kumar, D. Boovin, M. Samuel Karunakara Doss, S. Ganesh, K. Sanjai, A.V. Sri Sairam Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

IoT systems face significant security challenges due to their extensive attack surfaces and limited resources. To effectively counter these threats in real- time, intrusion detection algorithms must be both energy-efficient and compatible with the constrained computational capabilities of IoT devices (including memory, CPU, and storage). While deep learning approaches have proven effective for intrusion detection, their high computational demands make them impractical for resource-limited IoT devices. In this study, we propose developing a hierarchical model for IoT intrusion detection. this model will deploy machine learning (ML) algorithms tailored to the capabilities of IoT devices, while offloading more complex processing tasks to more powerful edge or cloud-based systems. these advanced systems will handle tasks such as updating ML model parameters and executing detection algorithms that exceed the capabilities of the IoT devices. Additionally, our approach will address not only traditional network attacks but also IoT-specific threats related to protocols such as REST, Zigbee, and MQTT. © 2024 ieee.

关键词： Network intrusion

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SynchroSim: An Integrated Co-simulation Middleware for Heterogeneous Multi-robot System 18

SynchroSim: An Integrated Co-simulation Middleware for Heter...

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18th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Dey, Emon Hossain, Jumman Roy, Nirmalya Busart, Carl Univ Maryland Baltimore Cty Ctr Real Time Distributed Sensing & Auton Dept Informat Syst Baltimore MD 21228 USA DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9781665495127

With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in real -world systems is tedious and may result in the wastage of valuable resources. Especially for heterogeneous multi -agent systems in battlefield environments where communication is critical in determining the system's behavior and usability. Due to the necessity of simulators of separate paradigms (co -simulation) to simulate such scenarios before deploying, synchronization between those simulators is vital. Existing works aimed at resolving this issue fall short of addressing diversity among deployed agents. In this work, we propose SynchroSim, an integrated co -simulation middleware to simulate a heterogeneous multi-robot system. Here we propose a velocity difference-driven adjustable window size approach with a view to reducing packet loss probability. It takes into account the respective velocities of deployed agents to calculate a suitable window size before transmitting data between them. We consider our algorithm specific simulator agnostic but for the sake of implementation results, we have used Gazebo as a Physics simulator and NS -3 as a network simulator. Also, we design our algorithm considering the Perception -Action loop inside a closed communication channel, which is one of the essential factors in a contested scenario with the requirement of high fidelity in terms of data transmission. We validate our approach empirically at both the simulation and system level for both line -of-sight (LOS) and non -line-of-sight (NLOS) scenarios. Our approach achieves a noticeable improvement in terms of reducing packet loss probability (approximate to 11%), and average packet delay (approximate to 10%) compared to the fixed window size-based synchronization approach.

关键词： Heterogeneous multi-robot systems NS-3 Gazebo Co-simulation Synchronization algorithm

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Scheduler Architecture and Key Features in distributed computing 4

Scheduler Architecture and Key Features in Distributed Compu...

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4th ieee international conference on Civil Aviation Safety and Information Technology, ICCASIT 2022

作者： Wang, Xuemin Han, Yang Yao, Xuyang Research Institute of China Telecom Corporation Limited Beijing China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

the cluster scheduler is an important component of modern distributed computing infrastructure, and it has evolved significantly in the last few years, so it has got much more attention by the industry and enterprises. With the development of information technology and the increasing demand of distributed applications, the cluster scheduler has been very complex and diversified. Among several resource scheduling system architecture, the design and work mechanism of their scheduler are very different. When we need to change the resource scheduling technology, from the built environment, the basic use, and then to customized modification according to the different business scenarios, each step may take us a lot of effort and cost. therefore, we need to understand the architecture principles and comprehend their advantages and disadvantages, so as to use the rules and experiences flexibly to optimize our system. this paper first states the classification and meaning of the core performance indicators of scheduling systems, and then summarizes the mainstream scheduler architectures of distributed systems, compares their differences and expounds their characteristics, and then gives a description of the key characteristics of schedulers. In short, it gives a more comprehensive analysis of schedulers. © 2022 ieee.

关键词： Cluster computing

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IRS-Aided Caching with Channel Power-Delay-Profile via DDPG and Self-Supervision Learning 9

IRS-Aided Caching with Channel Power-Delay-Profile via DDPG ...

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9th international conference on Computer and Communication systems (ICCCS)

作者： Huang, Huang Zhou, Fasheng Guangzhou Univ Sch Elect & Commun Engn Key Lab On Chip Commun & Sensor Chip Guangdong Hi Guangzhou Peoples R China

ISBN: (纸本)9798350350227;9798350350210

the 5G information age requires a large number of marginal nodes distributed around users to provide digital services. By caching and processing data at edge nodes close to users, edge caching technology can effectively collaborate with cloud computing and edge computing to improve the transmission efficiency as well as network response time. Meanwhile, intelligent reflective surface (IRS) is a promising technology for achieving high reductions in hardware cost and power consumption as compared to traditional relaying systems. It provides reliable and scalable backhaul transmission for base stations and access points in ultra-dense networks, which is in line with the requirements of 5G green technology. therefore, in this paper, we propose a collaborative edge caching method that effectively combines IRS-aided wireless communication and channel power-delay-profile to design joint caching decisions in a centralized manner through limited cache capacity, improving the quality of content delivery in information transmission. then we proposed a deep deterministic policy gradient learning method based on the framework of deep reinforcement learning along with self-supervision learning to predict IRS phases and collaborative caching strategies. the numerical simulation results show that the proposed algorithm has better convergence speed and learning accuracy than that of the existing algorithms, and is expected to be applied for future massive and dynamical content delivery applications.

关键词： Deep deterministic policy gradient (DDPG) edge caching intelligent reflecting surface average power-delay-profile(APDP) spectrum efficiency (SE) self-supervision learning

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A Development of Designing a sensor N/W for Retrieving the Data from WSN via SBT 4

A Development of Designing a Sensor N/W for Retrieving the D...

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4th ieee international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： thangaraj, S. John Justin Kumar, Neelam Sanjeev Saranya, A. Bhat, C. Rohith Kareem, Doaa Saadi Shareef, Hayder Saveetha School of Engineering Department of Cse Chennai India Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai Vadapalani26 India Amity University Amity School of Engineering and Technology Mumbai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Search engines are essential for getting information from web sites in the internet age. they use a variety of ranking algorithms, depending on content-based information extraction techniques or statistical searching strategies, to organise the retrieved results. It is still difficult, nevertheless, to comprehend the underlying information on each web page without opening each one individually. this challenge motivates the concept and implementation of an object-attribute-value (O-A-V) information extraction system as a web model. To help with future searches, this model functions as a user dictionary by helping to filter search terms in queries. To evaluate the first model, natural language processing (NLP) and semantic web (SW) queries in the form of English phrases are employed. Similar to this, content-based picture information extraction and retrieval against user queries is used by image search engines like Google Images. Using picture descriptions, a domain ontology is constructed in order to close the semantic gap between user expectations and image retrieval outcomes. the second suggested model looks at subject-predicate-object (S-P-O) interactions by analysing natural language user queries using an NLP parser algorithm. this extraction builds upon the O-A-V web model, which is ontology-based. the article presents a SPARQL auto-query generation module to auto-generate SPARQL questions by utilising S-P-O extraction and taking into account the intricacy of creating SPARQL queries from the standpoint of a user. the auto-generated SPARQL query is used to obtain photos after these queries are placed on the ontologies. systems for retrieving documents and images were developed and evaluated against industry standards in order to provide answers to these problems. the outcomes show improvements over baseline systems. © 2024 ieee.

关键词： Ontology

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Accelerating Time to Science using CRADLE: A Framework for Materials Data Science 30

Accelerating Time to Science using CRADLE: A Framework for M...

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30th international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Nihar, Arafath Ciardi, thomas G. Chawla, Rounak Akanbi, Olatunde Chaudhary, Vipin Wu, Yinghui French, Roger H. Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Case Western Reserve Univ Dept Mat Sci Cleveland OH USA

ISBN: (纸本)9798350383225

Modern materials science research problems present a challenge to data science and analytics as experiments generate Petabyte-scale spatiotemporal datasets that span a number of modalities and formats. Creating computing infrastructure and frameworks that support the scale and diversity of materials science data while remaining accessible for materials scientists to use is a non-trivial task. We have developed the Common Research Analytics and Data Lifecycle Environment (CRADLE) to solve the challenges of materials data science through a scalable research computing framework and cyber infrastructure that can (1) handle large-scale, heterogeneous datasets (2) provide a flexible toolbox for building machine learning pipelines that span from ingestion to model deployment (3) be accessible to research scientists with limited to extensive computational backgrounds and (4) utilize a myriad of low performance to high performance computer systems. CRADLE is a framework that integrates distributed systems like Hadoop and High-Performance computing (HPC) infrastructure to handle materials data at scale. this all enables the general materials data scientist to query Petabytes of data and train thousands of models in a parallel, distributed environment. We demonstrate three use cases for CRADLE to benchmark its capability to ingest and analyze spatiotemporal materials data at scale. these tasks span three data modalities: transforming 2.6 billion Photovoltaic time-series power measurements, training hundreds of deep learning models on Atomic Force Microscopy images, and ingesting 27 billion geospatial data points. CRADLE exemplifies an overarching framework that accelerates time to science, extends to other domains with similar challenges, and expands the horizon of data science and research.

关键词： scalable infrastructure distributed computing Hadoop AI4Science materials data science

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Optimization of throughput and Fairness Performance in Multi-Rate WLAN Network 4

Optimization of Throughput and Fairness Performance in Multi...

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4th international conference on Information Science, Parallel and distributed systems, ISPDS 2023

作者： Jin, Guonian Lu, Shejie Hu, Siping School of Computer Science and Technology Institute of Engineering and Technology Hubei University of Science and Technology Xianning437100 China

ISBN: (纸本)9798350337181

In ieee 802.11 wireless networks, as the number of network nodes increases, impact increase accordingly, channel utilization decreases, and aggregate throughput decreases, resulting in system function degradation. In response to this problem, this article proposes a solution: number nodes-based minimum contention window dynamic adjustment (NNMCWDA), which dynamically adjusts the minimum competition window to achieve channel resources' rational allocation. Simulation tests have shown that the NNMCWDA method can effectively work out the problem of channel utilization degradation, greatly improve fairness performance, and increase system throughput. On the side, what's particularly nice about this approach is that it requires only minor modifications over 802.11. In multi-rate networks, the minimum competition window is dynamically adjusted in line with the transfer rate. Its physical realization is very simple and handy, and can be achieved only by writing driver programs without any hardware modifications. therefore, it is easy to execute and popularize in an actual network. © 2023 ieee.

关键词： ieee Standards

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Combined Allocation of Renewable-Based distributed Generation and Soft Open Point into Distribution Networks in Presence of Plug-In Electric Vehicles: A Multi-Objective Framework 4

Combined Allocation of Renewable-Based Distributed Generatio...

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4th ieee international conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Pamshetti, Vijay Babu Zhang, Wei Infocomm Technology Cluster Singapore Institute of Technology 567739 Singapore Department of Electrical and Electronics Engineering 500075 India

ISBN: (纸本)9798350383997

this paper addresses the significant challenges of minimizing power loss and voltage deviation in distribution systems, exacerbated by the increasing prevalence of plug-in electric vehicles (PEVs). It introduces a novel multi-objective framework tailored to efficiently integrate Renewable- Based distributed Generation and soft open point (SOP) technologies into distribution networks, considering the presence of PEVs. the primary objective is to concurrently minimize power losses and voltage deviation. the study integrates practical voltage-dependent load models and PEV loads into the analysis. Employing the ϵ constraint method, the paper addresses the multi-objective optimization problem, implemented using the GAMS and MATLAB interfacing environment. through numerical simulations on the ieee 33-bus distribution network, the authors demonstrate the effectiveness of the proposed multi-objective optimization scheme. Test results underscore the significance of combined allocation of RES and SOP, revealing a notable reduction of approximately 16.6% in power loss and 15.59% in voltage deviation within the distribution network. this approach ensures a reliable and efficient power supply, essential for modern energy systems. © 2024 ieee.

关键词： Power distribution networks

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Anti-DDoS Attacks Strategy of SDN Data Plane with Data Augmentation Based on P4 25

Anti-DDoS Attacks Strategy of SDN Data Plane with Data Augme...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Wenxiu Jing, Shan Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan China Quan Cheng Laboratory Jinan China

ISBN: (纸本)9798350330014

As Software Defined Networking (SDN) becomes increasingly prevalent, the risk of distributed Denial of Service (DDoS) attacks targeting SDN also grows. Specifically, the SDN data plane, consisting of simple forwarding devices, is more susceptible to DDoS attacks launched by malicious actors. these attacks can impose tremendous load on the SDN, potentially leading to its complete collapse in severe cases. the traditional SDN architecture based on the OpenFlow Protocol falls short in meeting the requirements for programming the data plane. To address this challenge and improve the flexibility of SDN architectures, the Programming Protocol-Independent Packet Processors (P4) has emerged. However, existing solutions, primarily based on Statistical Learning, Machine Learning, and Deep Learning, require further enhancement in terms of accuracy, complexity, and latency. In this study, we utilize the P4 language to implement a programmable data plane and propose the P4-CACNN model for detecting and defending against DDoS attacks on the data plane. Firstly, we employ an appropriate attention mechanism to enhance the processing of incoming traffic. Subsequently, the data is fed into a discriminator consisting of a Convolutional Neural Network (CNN) to classify whether it constitutes attack traffic. Experimental results demonstrate that our proposed model achieves a remarkable 98.99% accuracy in detecting DDoS attacks on the SDN data plane, while maintaining low latency. © 2023 ieee.

关键词： Denial-of-service attack

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distributed Training Based on Horizontal Scaling for Food Image Classification 4

Distributed Training Based on Horizontal Scaling for Food Im...

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4th international conference on Embedded and distributed systems, EDiS 2024

作者： El Amin, Gasbaoui Mohammed Soumia, Benkrama Mostefa, Bendjima Sofiane, Abden Ikram, Kourtiche Tahri Mohammed University Laboratory of Tit Mathematics and Computer Science Department Bechar Algeria

ISBN: (纸本)9798331519872

Deep learning models for food image classification rely on vast amounts of data to effectively recognize and differentiate between various food items. However, training these models on such extensive datasets presents significant computational challenges. Big data and distributed training are inherently complementary in addressing these challenges. Big data involves large volumes of information that traditional single-machine processing cannot handle efficiently. distributed training offers a solution by enabling machine learning models to scale across multiple machines or nodes, thereby more effectively processing and learning from extensive datasets. By partitioning tasks into smaller chunks processed in parallel, distributed training optimizes computational resources, ensuring efficient analysis of big data while maintaining fault tolerance and performance scalability. this paper explores various strategies for distributed training and communication between GPUs. We compare the performance and utilization of a single P100 GPU versus two T4 GPUs on the Food-11 dataset, focusing on accuracy and training time. Our results indicate that using two GPUs yields slightly better performance than a single GPU, reducing the training time by 125 seconds, decreasing the validation dataset loss by 0.02, and increasing the accuracy by 1%. the overall accuracy achieved is 92%, with a corresponding loss of 0.28. © 2024 ieee.

关键词： big data data distribution deep learning food images GPUs parallel processing PyTorch

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