Related Experiment Video
Updated: Oct 1, 2025
08:25
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
Published on: May 7, 2019
9.1K
A Survey of techniques for fine-grained web traffic identification and classification
Xiaolin Gui1, Yuanlong Cao1, Ilsun You2
1School of software, Jiangxi Normal University, Nanchang 330027, China.
Mathematical Biosciences and Engineering : MBE
|March 4, 2022
Summary
Modern networks require advanced solutions. This paper overviews fine-grained network traffic identification, a key technology for managing complex networks, and reviews its applications in wired, mobile, and malware contexts.
Area of Science:
- Computer Science
- Network Engineering
Background:
- Modern networks have grown in scale and complexity.
- Traditional traffic identification methods are insufficient for current network demands.
Purpose of the Study:
- To provide a comprehensive overview of fine-grained network traffic identification.
- To conduct a literature review on this topic across different network types.
Main Methods:
- Literature review of fine-grained network traffic identification.
- Analysis across wired networks, mobile networks, and malware traffic identification.
Main Results:
- Fine-grained network traffic identification is an effective solution for network resource management.
- Identified key challenges and future research directions in the field.
Conclusions:
- Fine-grained network traffic identification is crucial for modern communication networks.
- Further research is needed to address existing challenges and explore future prospects.
Related Concept Videos
01:28Methods of Classification and Identification
294
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
294
01:29Aggregates Classification
402
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
402
01:13Special Staining Techniques
425
Specialized staining techniques play a vital role in microbiology by enabling the visualization of specific bacterial structures that remain undetectable with standard microscopy methods. These techniques not only enhance the structural visualization of bacterial cells but also provide critical insights into their pathogenicity and classification. Additionally, they support diagnostic and research endeavors in microbiology by identifying key bacterial features.Capsule Staining for Virulence...
425
01:30Classification of Signals
987
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
987
01:26Classification of Systems-I
348
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
348