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Design of network Monitoring System for IoT and Autonomous network using Python 2

Design of Network Monitoring System for IoT and Autonomous N...

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2nd ieee International conference on Vision Towards Emerging Trends in Communication and networking Technologies, ViTECoN 2023

作者： Suchitra, V. Kumar, Rajesh Saha, Pradip Nayak, D.B. Ratha, Hare Krishna DRDO Communication Directorate Integrated Test Range Chandipur India

ISBN: (纸本)9798350347982

The design and development of an open standard, scalable platform, and interoperable, network Monitoring System (NMS) is always an inspiration and challenging for IoT and Autonomous systems which are namely deployed over a local and remote environment. The developed system is a novel approach towards scalable and open platform-based NMS using Python programming language. It has a feature to mark the performance and faults of all connected nodes over a network. It utilizes Simple network Management Protocol (SNMP) and a Syslog-based monitoring module. With the quality of the real-time monitoring system, it ensures an individual link status and its summary and depicts its updates in real-time GUI updater, and store data in MySQL database. It provides easy visualization of network stations and allows the concerned to take appropriate action accordingly. © 2023 ieee.

关键词： Python

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GraNNDis: Fast Distributed Graph Neural network Training Framework for Multi-Server Clusters 24

GraNNDis: Fast Distributed Graph Neural Network Training Fra...

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International conference on Parallel architectures and Compilation Techniques (PACT)

作者： Song, Jaeyong Jang, Hongsun Lim, Hunseong Jung, Jaewon Kim, Youngsok Lee, Jinho Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (数字)9798400706318

ISBN: (纸本)9798400706318

Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning. While many distributed GNN training frameworks have been proposed to increase the training throughput, they face three limitations when applied to multi-server clusters. 1) They suffer from an inter-server communication bottleneck because they do not consider the inter-/intra-server bandwidth gap, a representative characteristic of multi-server clusters. 2) Redundant memory usage and computation hinder the scalability of the distributed frameworks. 3) Sampling methods, de facto standard in mini-batch training, incur unnecessary errors in multi-server clusters. We found that these limitations can be addressed by exploiting the characteristics of multi-server clusters. Here, we propose GraNNDis, a fast distributed GNN training framework for multi-server clusters. Firstly, we present Flexible Preloading, which preloads the essential vertex dependencies server-wise to reduce the low-bandwidth inter-server communications. Secondly, we introduce Cooperative Batching, which enables memory-efficient, less redundant mini-batch training by utilizing high-bandwidth intra-server communications. Thirdly, we propose Expansion-aware Sampling, a cluster-aware sampling method, which samples the edges that affect the system speedup. As sampling the intra-server dependencies does not contribute much to the speedup as they are communicated through fast intra-server links, it only targets a server boundary to be sampled. Lastly, we introduce One-Hop Graph Masking, a computation and communication structure to realize the above methods in multi-server environments. We evaluated GraNNDis on multi-server clusters, and it provided significant speedup over the state-of-the-art distributed GNN training frameworks. GraNNDis is open-sourced at https://***/AISSNU/GraNNDis_Artifact to facilitate its use.

关键词： Distributed Systems Systems for Deep Learning Graph Neural network Training Distributed Graph Neural network Training

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Scalable Forward Reachability Analysis of Multi-Agent Systems with Neural network Controllers 62

Scalable Forward Reachability Analysis of Multi-Agent System...

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62nd ieee conference on Decision and Control (CDC)

作者： Gates, Oliver Newton, Matthew Gatsis, Konstantinos Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9798350301243

Neural networks (NNs) have been shown to learn complex control laws successfully, often with performance advantages or decreased computational cost compared to alternative methods. Neural network controllers (NNCs) are, however, highly sensitive to disturbances and uncertainty, meaning that it can be challenging to make satisfactory robustness guarantees for systems with these controllers. This problem is exacerbated when considering multi-agent NN-controlled systems, as existing reachability methods often scale poorly for large systems. This paper addresses the problem of finding overapproximations of forward reachable sets for discrete-time uncertain multi-agent systems with distributed NNC architectures. We first reformulate the dynamics, making the system more amenable to reachablility analysis. Next, we take advantage of the distributed architecture to split the overall reachability problem into smaller problems, significantly reducing computation time. We use quadratic constraints, along with a convex representation of uncertainty in each agent's model, to form semidefinite programs, the solutions of which give overapproximations of forward reachable sets for each agent. Finally, the methodology is tested on two realistic examples: a platoon of vehicles and a power network system.

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Soft open Point Planning Method for Active Distribution network Based on Generative Adversarial network 7

Soft Open Point Planning Method for Active Distribution Netw...

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7th ieee conference on Energy Internet and Energy System Integration, EI2 2023

作者： Wang, Hao Lin, Qiyou Wang, Hongxing Ge, Yuan Yu, Nuo Wang, Shinong School of Electrical Engineering Anhui Polytechnic University Wuhu China State Grid Anhui Electric Power Co. Ltd Wuhu Fanchang District Power Supply Company Wuhu China State Grid Anhui Electric Power Co. Ltd Wuwei City Power Supply Company Wuhu China School of Electrical Engineering Anhui Polytechnic University Anhui Institute of Future Technology Wuhu China

ISBN: (纸本)9798350345094

With the continuous increase of the penetration rate of distributed generation (DG) in the distribution network, higher requirements have been put forward for the dependable and cost-effective operation of the distribution network. In order to address the impact caused by the intermittent output of a large number of DGs on the distribution network, this article proposes a planning method for soft open point (SOP) using generative adversarial network (GAN). First, to accurately simulate the operating characteristics of DG, a GAN is employed for sce-nario generation. Then, operational constraints for distribution networks with SOP are proposed, with the objective function being the minimization of annual total cost. By relaxing and linearizing the original non-convex and non-linear constraints, a second-order cone programming model is derived, and the objective function is solved under the constraints. Finally, the proposed scenario generation algorithm and planning model are analyzed and veriffed on the ieee 33- node test system. © 2023 ieee.

关键词： Second-order cone programming

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Memory-Guided Coordinate Encoding network for Anomaly Detection

Memory-Guided Coordinate Encoding Network for Anomaly Detect...

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2023 ieee International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Xingang Zhang, Hong Cao, Rui Zhou, Jinyan Lu, Xingchao Jinan China

ISBN: (纸本)9798350337020

Video anomaly detection remains challenging due to the complexity of visual scenes. Video surveillance is often fixed lens, and existing approaches, either using autoencoder architectures or generative adversarial network models, lack encoding of dynamic and static information in feature extraction to emphasize information-rich features. Based on this, we improve the encoder architecture and propose a memory-guided coordinate encoding network based on extensive experiments to introduce a coordinate attention module to improve the U-Net network and enhance dynamic entity representation. Considering the diversity of abnormal events, we use the memory module to record the prototype patterns of normal features and propose feature discretization loss and feature aggregation loss to make a compact representation between features and separation between feature terms to improve the accessibility and prediction accuracy of the memory module. Our experimental results on three open standard datasets show that our model outperforms the state-of-the-art methods. © 2023 ieee.

关键词： anomaly detection attention coordinate encoding memory module

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Real-Time Tracking Control Using Adaptive Dynamic programming for Underwater Vehicles 21

Real-Time Tracking Control Using Adaptive Dynamic Programmin...

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21st International conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2024

作者： Duran, Claudia Rigel Palacios, Filiberto Munoz Espinoza, Eduardo S. Trinidad, Benjamin Nicolas DIEM CINVESTAV Mexico City Mexico DIEM IxM CONAHCYT-CINVESTAV Mexico City Mexico

ISBN: (纸本)9798350377545

This manuscript presents the real-Time experiments of a model-free nonlinear controller based on Adaptive Dynamic programming (ADP) for an Autonomous Underwater Vehicle (AUV). These types of vehicles exhibit significant coupled nonlinearities and parameter uncertainties. To address this, our approach introduces a data-driven scheme, where open-loop data are collected and used to train a differential neural network for identifying the vehicle's dynamics. Based on the identified model, an ADP scheme with an actor-critic structure is implemented to conduct trajectory tracking experiments for yaw and depth dynamics. Additionally, this study examines the implications of using the ADP algorithm and offers insights for necessary adaptations to achieve successful implementation on an open-source commercial underwater vehicle within the ROS environment, thereby bridging the gap between theoretical concepts and practical experimentation. © 2024 ieee.

关键词： Linear systems

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ProSLICE: An open RAN-based approach to Programmable RAN Slicing

ProSLICE: An Open RAN-based approach to Programmable RAN Sli...

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ieee Global Communications conference (GLOBECOM)

作者： Kak, Ahan Pham, Van-Quan Thieu, Huu-Trung Choi, Nakjung Nokia Bell Labs Network Syst & Secur Res Murray Hill NJ 07974 USA

ISBN: (纸本)9781665435406

The increasing popularity of programmable wireless networks has led to efforts by both academia as well as industry to redesign access networks based on the disaggregated open Radio Access network (O-RAN) concept. However, the absence of a development platform for prototyping O-RAN use cases and the perceived complexity of the O-RAN specification have led to a stagnation of systems research efforts in this domain with a disproportionate focus on monolithic RAN architectures. With a view to overcoming these challenges, this paper introduces the ProSLICE platform, a full-scale realization of the complete O-RAN specification based on purely open source components along with several enhancements and extensions for fine-grained network control and ease of use. Key contributions include a fully disaggregated O-RAN-compliant RAN, a custom O-RAN service model in support of network slicing, statistics and configuration applications for the O-RAN RAN Intelligent Controller (RIC), and a comprehensive use case-based performance evaluation.

关键词： Specifications

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Developing Adaptive Deep Learning Strategies for 5G network Management 15

Developing Adaptive Deep Learning Strategies for 5G Network ...

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15th International conference on Computing Communication and networking Technologies, ICCCNT 2024

作者： Patil, Shrinivas Kumar, Viranshu Goyal, Manish Kumar Karnataka Bangalore India Department of Mechnical Engineering ARKA JAIN University Jharkhand Jamshedpur India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (纸本)9798350370249

This paper affords a unique method for growing deep mastering strategies for 5G community control. It leverages state-of-the-art deep neural community architectures and superior technologies inclusive of hierarchical learning, reminiscence networks, and differentiable programming to allow the machine to operate more efficaciously in an ever-changing surrounding. The gadget includes two principal components: an offline schooling community and a web auxiliary network. Each additive is related, permitting the net network to refine the educated parameters to adapt to changing occasions. The gadget is designed to deal with some of the maximum hard issues in 5G community management, along with excessive degree choice-making, resource optimization, and exceptional service enforcement. Experiments and simulations are used to evaluate the performance enhancements finished via applying the proposed technique. The consequences display that the proposed method should improve the general overall performance of 5G networks drastically. The proposed method may be used in a selection of network management packages, which include network virtualization, visitor engineering, and self-organizing networks. © 2024 ieee.

关键词： 5G mobile communication systems

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Optimizing IoT network Intrusion Detection: A Deep Learning Approach 7

Optimizing IoT Network Intrusion Detection: A Deep Learning ...

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7th conference on Cloud and Internet of Things

作者： Alam, Khorshed Monir, Md Fahad Hassan, Zoheb Habib, Md Tarek Independent Univ Bangladesh IUB Dept Comp Sci Engn Dhaka Bangladesh Virginia Tech Dept Elect & Comp Engn Blacksburg VA USA Univ Laval Dept Elect & Comp Engn Laval PQ Canada

ISBN: (纸本)9798350356717;9798350356724

network Intrusion Detection System (NIDS) serves as a essential component in data protection by monitoring computer networks for threats that can bypass conventional defenses such as malware and hackers. Deep learning (DL) techniques provide a promising approach for analyzing raw IoT network data to identify subtle patterns indicative of intrusion attempts. This study addresses a crucial research gap by developing a Deep Convolutional Neural network (DCNN) model specifically designed for the efficient detection of stealthy and polymorphic variants while reducing false positives. Utilizing the NF-ToN-IoT dataset, the proposed model achieves outstanding performance metrics on test data, with an accuracy of 0.9923, precision of 0.9925, recall of 0.9979, and F1 score of 0.9952. To comprehensively evaluate the robustness of the model, a multi-dataset validation strategy is employed. The model is retrained and assessed on established benchmark datasets on IoT networks, including NF-UNSW-NB15, NF-UNSW-NB15-v2 and NF-BoT-IoT, demonstrating exceptional performance. Furthermore, the significance of the contribution is validated by comparing the proposed model against previously established architectures such as CNN+BiLSTM, DNN, GRU+RNN, and CNN+LSTM using the NF-ToN-IoT dataset. The proposed model consistently outperforms these prior models, highlighting its efficacy and advancements in the field. Additionally, an ablation study is conducted to analyze the individual components of the Deep CNN model, providing insights into their contributions towards detecting malware traffic and offering guidance for optimizing future NIDS models in the cybersecurity domain. Making our work available open-source on https://***/codewithkhurshed/IDS-IUB can enhance its accessibility and promote future research opportunities in network Intrusion Detection.

关键词： network Protection Intrusion Detection Mechanism Deep Convolutional Neural networks Cyber Defense Malware Identification IoT

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network Intrusion Detection System Using Ensemble Methods and Deep Neural network 9

Network Intrusion Detection System Using Ensemble Methods an...

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9th International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Bandari, Sheethal Rangdal, Aakanksha Rukmanna Manisha, Budhi Shailaja, M. Sreenidhi Institute of Science And Technology Department of Electronics And Computer Engineering Hyderabad India

ISBN: (纸本)9798350365092

Today, we find almost everybody using internet and with increase in demand of computer networking, Hackers are taking advantage of this situation and trying to intrude into the networks and disturb the networks thus by injecting malwares into it. It is very important to identify whether the network is free from malware or not in order to ensure secrecy of valuable data. Intrusion Detection Systems (IDS) are some of the most prominent technologies for administrating and looking after security issues in the network. With rapidly changing network traffic data, one classifier is not enough to fend off modern network intruders. In this work, ensemble classifiers are used to classify data based on binary and multiclass. We took this as a major challenge to increase accuracy rates of detection for individual attack types and all types of attacks. This allows us to identify attacks and specific attack categories. We've chosen to contrast other works by opting for Artificial networks as well. Python being the most versatile programming language in recent times, we chose IDS-ML an open source code repository in order to develop IDS from public network traffic records that can protect modern networks with high efficiency and accuracy. © 2024 ieee.

关键词： Malware

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