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Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Network Function of a Circuit01:25

Network Function of a Circuit

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Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Zones of Protection01:16

Zones of Protection

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In power systems, the entire setup is divided into protective zones to isolate faults and protect the rest of the network. These zones include generators, transformers, buses, transmission lines, distribution lines, and motors. Each zone can be visualized as a separate room in a house, with each room protected by its own circuit breaker.
Protective zones are defined by closed dashed lines, containing one or more components. A key characteristic of these zones is the strategic placement of...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Related Experiment Video

Updated: Oct 3, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

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Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN

Senthil Prabakaran1, Ramalakshmi Ramar2, Irshad Hussain3

  • 1Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore 641032, Tamil Nadu, India.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary

This study enhances network security by using machine learning in Software Defined Networking and Network Function Virtualization. Bayesian Network algorithms achieved 92.87% accuracy in predicting cyber threats.

Keywords:
SDNFVattack predictionbayesian networkdecision tablefirewallmachine learningnetwork function virtualizationsoftware defined network

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Software Defined Networks (SDN) and Network Function Virtualization (NFV) offer enhanced network management and security.
  • Traditional security measures struggle against sophisticated intruder assaults targeting multiple socket addresses.
  • Virtualized Network Functions (VNFs) deployed in SDN networks improve scalability and security.

Purpose of the Study:

  • To design a Software Defined Network Function Virtualization (SDNFV) network for improved performance and security.
  • To deploy stateful firewall services as VNFs for enhanced network scalability and threat mitigation.
  • To leverage machine learning for predicting and preventing network attacks.

Main Methods:

  • Implementation of an SDNFV network architecture.
  • Deployment of stateful firewall services as Virtualized Network Functions (VNFs).
  • Training and evaluation of machine learning algorithms (Bayesian Network, Naive-Bayes, C4.5, Decision Table) on network threat intelligence data.

Main Results:

  • The Bayesian Network algorithm demonstrated the highest prediction accuracy at 92.87%.
  • Naive-Bayes, C4.5, and Decision Tree algorithms achieved accuracies of 87.81%, 84.92%, and 83.18%, respectively.
  • Analysis of a large dataset including 451K login attempts from 178 countries, 70K source IPs, and 40K source ports.

Conclusions:

  • Machine learning, particularly the Bayesian Network algorithm, significantly improves the prediction of network attack targets in SDNFV environments.
  • The proposed SDNFV framework with VNF-based firewalls offers a scalable and effective solution for network security.
  • The study highlights the potential of ML in identifying malicious network activities and enhancing cybersecurity defenses.