AI Anomaly Detection for Cloudified Mobile Core Architectures.pdf
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monitoring such as e-health care services, smart homes, or even vehicular area networks (VAN). For proof
of concept, we have implemented a REST based Web services on an IP based low power WSN test bed,
which enables data access from anywhere. The alert feature has also been implemented to notify users via
email or tweets for monitoring data when they exceed values and events of interest.
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present this using Machine Learning in Software-Defined Networking technology. The first task consists of
extracting relevant information from the Data flow generated from a SDN and using them to learn, to
predict and to accurately identify the optimal destination OVS using Reinforcement Learning and QLearning Algorithm. The second task consists to select this using our hybrid orchestrator the optimal
Intelligent SDN controllers with Supervised Learning. Therefore, we propose as a solution using Intelligent
Software-Defined Networking controllers (SDN) frameworks, OpenFlow deployments and a new intelligent
hybrid Orchestration for multi SDN controllers. After that, we feeded these feature to a Convolutional
Neural Network model to separate the classes that we’re working on. The result was very promising the
model achieved an accuracy of 72.7% on a database of 16 classes. In any case, this paper sheds light to
researchers looking for the trade-offs between SDN performance and IA customization
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This paper presents an overview of our learning-based orchestrator for intelligent Open vSwitch that we present this using Machine Learning in Software-Defined Networking technology. The first task consists of extracting relevant information from the Data flow generated from a SDN and using them to learn, to predict and to accurately identify the optimal destination OVS using Reinforcement Learning and QLearning Algorithm. The second task consists to select this using our hybrid orchestrator the optimal
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Software-Defined Networking controllers (SDN) frameworks, OpenFlow deployments and a new intelligent
hybrid Orchestration for multi SDN controllers. After that, we feeded these feature to a Convolutional Neural Network model to separate the classes that we’re working on. The result was very promising the model achieved an accuracy of 72.7% on a database of 16 classes. In any case, this paper sheds light to
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www.ijerd.com
The document proposes a novel distributed network architecture for enterprise network vulnerability assessment using CORBA. It discusses limitations of current vulnerability assessment solutions, such as inflexibility in distributing scanning tasks across multiple scanners. The proposed architecture would dynamically assign scanning tasks based on scanner availability to improve efficiency and reliability. It would also decrease the size of individual scanning tasks to speed up task reassignment in case of issues. The architecture follows CORBA standards to define interfaces for communication between distributed components for vulnerability detection and remediation across an enterprise network.
Presentation1.pptx
This document describes a proposed system for detecting cyber attacks using Bayesian inference. It begins with an introduction to the problem of credit/debit card theft and existing physical unclonable functions. It then discusses the disadvantages of existing cyber attack detection systems, such as performance issues and high false positive rates. The proposed system builds a directed acyclic graph to represent the probability distribution of variables related to cyber attacks. It will use modules for data collection, preprocessing, model training/testing, and attack detection. The system will be implemented in Python using frameworks like Django and evaluated using algorithms like random forest, artificial neural networks, and support vector machines.
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How to Build a Module in Odoo 17 Using the Scaffold Method
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
DRUGS AND ITS classification slide share
Any substance (other than food) that is used to prevent, diagnose, treat, or relieve symptoms of a
disease or abnormal condition
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama UniversityAkanksha trivedi rama nursing college kanpur.
Natural birth techniques are various type such as/ water birth , alexender method, hypnosis, bradley method, lamaze method etcবাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
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আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Digital Artifact 1 - 10VCD Environments Unit
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Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.Recently uploaded (20)
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
How to Build a Module in Odoo 17 Using the Scaffold Method
How to Build a Module in Odoo 17 Using the Scaffold Method
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
AI Anomaly Detection for Cloudified Mobile Core Architectures.pdf
- 1. AI Anomaly Detection for Cloudified Mobile Core Architectures Abstract IT systems monitoring is a crucial process for managing and orchestrating network resources, allowing network providers to rapidly detect and react to most impediment causing network degradation. However, the high growth in size and complexity of current o solutions to process huge amounts of data (including alarms) reliably and swiftly. Further, as the network becomes progressively more virtualized, the hosting of NFV on cloud environments adds a magnitude of possible bottlenecks outside the control of the service owners. In this paper, we propose two deep learning anomaly detection solutions that leverage service exposure and apply it to automate the detection of service degradation and root cause discovery in a cloudi ETSI OSM. A testbed is built to validate these AI models. The testbed collects monitoring data from the OSM monitoring module, which is then exposed to the external AI anomaly detection modules, tuned to identify AI Anomaly Detection for Cloudified Mobile Core Architectures IT systems monitoring is a crucial process for managing and orchestrating network resources, allowing network providers to rapidly detect and react to most impediment causing network degradation. However, the high growth in size and complexity of current operational networks (2022) demands new solutions to process huge amounts of data (including alarms) reliably and swiftly. Further, as the network becomes progressively more virtualized, the hosting of NFV on cloud environments adds a magnitude of possible bottlenecks outside the control of the service owners. In this paper, we propose two deep learning anomaly detection solutions that leverage service exposure and apply it to automate the detection of service degradation and root cause discovery in a cloudified mobile network that is orchestrated by ETSI OSM. A testbed is built to validate these AI models. The testbed collects monitoring data from the OSM monitoring module, which is then exposed to the external AI anomaly detection modules, tuned to identify the anomalies AI Anomaly Detection for Cloudified IT systems monitoring is a crucial process for managing and orchestrating network resources, allowing network providers to rapidly detect and react to most impediment causing network degradation. However, the high growth in perational networks (2022) demands new solutions to process huge amounts of data (including alarms) reliably and swiftly. Further, as the network becomes progressively more virtualized, the hosting of NFV on cloud environments adds a magnitude of possible bottlenecks outside the control of the service owners. In this paper, we propose two deep learning anomaly detection solutions that leverage service exposure and apply it to automate the detection of service degradation and fied mobile network that is orchestrated by ETSI OSM. A testbed is built to validate these AI models. The testbed collects monitoring data from the OSM monitoring module, which is then exposed to the anomalies
- 2. and the network services causing them. The deep learning solutions are tested using various artificially induced bottlenecks. The AI solutions are shown to correctly detect anomalies and identify the network components involved in the bottlenecks, with certain limitations in a particular type of bottlenecks. A discussion of the right monitoring tools to identify concrete bottlenecks is provided.