Machine Learning For Network Traffic and Video Quality Analysis: Develop and Deploy Applications Using JavaScript and Node.js

Table of Contents....4

About the Authors....11

About the Technical Reviewer....13

Chapter 1: Introduction....14

1.1 Overview of Network Traffic Monitoring and Analysis....14

1.1.1 Importance of NTMA....17

1.1.2 Key Objectives of NTMA....18

1.1.3 Network Traffic Components....19

1.1.4 NTMA Techniques and Methodologies....21

1.1.5 Challenges of NTMA....24

1.1.6 Use Cases of NTMA....25

1.1.7 Emerging Trends in NTMA....27

1.1.8 Bridging the Gap between NTMA and User Experience....27

1.2 Overview of Video Quality Assessment....28

1.2.1 Significance of VQA....30

1.2.2 Factors Affecting Video Quality....31

1.2.3 Evolution of VQA Approaches....33

1.2.4 Real-World Applications of VQA....36

1.2.5 Challenges in VQA....37

1.2.6 Emerging Trends in VQA....39

1.3 Machine Learning in JavaScript....40

1.3.1 Introduction to Machine Learning....40

1.3.2 Coupling JavaScript with Machine Learning....42

1.3.3 Data Preparation and Preprocessing in JavaScript....43

1.3.4 Supervised Learning with JavaScript....45

1.3.5 Unsupervised Learning with JavaScript....46

1.3.6 Deep Learning in JavaScript....47

1.3.7 Deploying Machine Learning Models in Web Applications....49

1.4 Node.js and Networking....51

1.5 Book Overview....52

1.6 References – Chapter 1....54

Chapter 2: Network Traffic Monitoring and Analysis....63

2.1 NTMA Fundamentals....63

2.1.1 Data Sources and Collection....66

2.1.2 Key Metrics....67

2.1.3 Data Preprocessing and Cleaning....69

2.1.4 Network Topology and Architecture....70

2.1.5 Data-Driven Analytics....73

2.1.6 Supervised Learning for Traffic Classification....74

2.1.7 Unsupervised Learning for Anomaly Detection....75

2.1.8 Predictive Analytics....76

2.1.9 Real-time AI-Based Decision Support....78

2.2 Existing NTMA Applications....80

2.2.1 SolarWinds NetFlow Traffic Analyzer....80

2.2.2 Paessler PRTG Network Monitor....80

2.2.3 Wireshark....82

2.2.4 ManageEngine NetFlow Analyzer....84

2.2.5 Site24x7 Network Monitoring....85

2.2.6 Prometheus....86

2.2.7 Commercial vs. Open-Source Solutions....88

2.2.8 Challenges and Considerations....88

2.3 State-of-the-Art Review of NTMA....90

2.3.1 Background of NTMA....90

2.3.2 The Rise of Machine Learning....90

2.3.3 Machine Learning Algorithms to Classify Network Traffic....91

2.3.4 Machine Learning Algorithms to Predict Network Traffic....96

2.4 Summary....101

2.5 References – Chapter 2....101

Chapter 3: Video Quality Assessment....109

3.1 VQA Fundamentals....109

3.1.1 Video Quality Metrics....111

3.1.2 Human Perception in Video Quality....113

3.1.3 Video Quality Attributes....114

3.1.4 The Optimal VQA Strategy....116

3.1.5 Quality of Experience (QoE) Metrics....117

3.1.6 Quality of Service (QoS) Metrics....119

3.1.7 Quality of Performance (QoP) Metrics....120

3.1.8 Subjective VQA....120

3.1.9 Objective VQA....121

3.1.10 Quality Metrics for Network, Video, and Streaming....122

3.1.11 Video Quality Databases and Benchmarking....123

3.1.12 Temporal and Spatial Considerations in VQA....124

3.1.13 VQA for Evolving Video Content....125

3.2 Existing VQA Applications....127

3.2.1 Sentry by Telestream....127

3.2.2 Real-Time Media Assessment (RTMA) by ThinkTel....128

3.2.3 Witbe....128

3.2.4 ViCue Soft....129

3.2.5 AccepTV Video Quality Monitor....130

3.2.6 VQEG Image Quality Evaluation Tool (VIQET)....130

3.3 State-of-the-Art Review of VQA....132

3.3.1 Background of VQA....132

3.3.2 Machine Learning in VQA....133

3.3.3 Machine Learning Algorithms to Analyze Video Quality in Multimedia Communications....134

3.4 Summary....143

3.5 References – Chapter 3....143

Chapter 4: Machine Learning Techniques for NTMA and VQA....153

4.1 Classification Model for NTMA....153

4.1.1 Data Collection for Classification....154

4.1.2 K-Nearest Neighbor (KNN) Algorithm....156

4.1.3 Data Preparation for Classification....156

4.1.4 Shorthand Example for KNN....157

4.2 Prediction Model for NTMA....158

4.2.1 Multilayer Perceptron (MLP) Algorithm....158

4.2.2 Hyperparameters....159

4.2.3 Data Preparation for Time-Series Prediction....160

4.2.4 Sliding Window Concept....161

4.2.5 MLP for Time-Series Network Traffic Prediction....162

4.2.6 Short-hand Example for MLP....163

4.3 SVM for VQA....169

4.3.1 Blind Image Quality Assessment Using Distortion Aggravation....170

4.3.2 Preliminary Steps....170

4.3.3 Extraction of LBP Features....171

Process and Equations....171

Illustrations and Coding Procedure....172

4.3.4 Distortion Aggravation....177

JPEG Compression....177

JPEG2000 Compression....178

Gaussian Blur....178

White Noise....178

4.3.5 Similarity Index....179

4.3.6 Scaling....181

4.3.7 Using SVM for Prediction....183

4.4 Summary....183

4.5 References – Chapter 4....184

Chapter 5: NTMA Application with JavaScript....187

5.1 System Model for NTMA....187

5.1.1 Components and Functionalities....189

5.1.2 Prediction and Classification of Network Traffic....191

5.1.3 NTMA Application Layout....191

5.1.4 Client–Server Interaction....193

5.2 Client Program Structure for NTMA....194

5.2.1 Configuring Extension Settings and Permissions....195

5.2.2 Configuring the Background Script....197

5.2.3 Building the User Interface....198

File Functionality....198

Libraries and Required Resources....199

Creating the Document Structure....200

Adding the Document Details and References to External Resources....200

Adding the Graph and Dashboard Components....201

Styling the Components through Internal CSS....205

Adding the User Interface to Google Chrome....210

Visualizing the User Interface....212

5.2.4 Building the Client Script....213

File Functionality....213

Creating the Script Structure....214

Initializing and Configuring the Chart on Page Load....215

Real-Time WebSocket Communication and Dynamic Graph Updates....219

Real-Time Chart Update Mechanism....225

5.3 Server Program Structure for NTMA....226

5.3.1 Libraries and Required Resources....229

5.3.2 Adding Libraries....229

5.3.3 Declaring Global Variables....230

5.3.4 Fetching Local Databases....231

5.3.5 Creating a WebSocket Server....232

5.3.6 Listening for a Client Connection Request....233

Getting Messages from the Client....234

Measuring Latency....235

Measuring Network Traffic and Sending Real-Time Metrics to the Client....237

5.3.7 Method for Time-Series Prediction with MLP Regression....241

5.3.8 Method for Calculating the QoS Score....248

5.3.9 Method for Classifying the Device Activity....251

5.4 NTMA Application Testing and Deployment....256

5.5 Summary....259

5.6 References – Chapter 5....261

Chapter 6: Video Quality Assessment Application Development with JavaScript....262

6.1 System Model for VQA....262

6.1.1 Components and Functionalities....264

6.1.2 Prediction of an MOS Score for Video Quality....266

6.1.3 VQA Application Layout....266

6.1.4 Client–Server–Servlet Interaction....267

6.2 Client Program Structure for VQA....268

6.2.1 Configuring Extension Settings and Permissions....269

6.2.2 Configuring the Background Script....271

6.2.3 Building the User Interface....271

File Functionality....272

Libraries and Required Resources....273

Creating the Document Structure....273

Adding the Document Details and References to External Resources....274

Adding the Graph and Dashboard Components....275

Styling the Components through Internal CSS....279

Adding the User Interface to Google Chrome....282

Visualizing the User Interface....284

6.2.4 Building the Client Script....285

File Functionality....285

Creating the Script Structure....287

Initializing and Configuring the Chart on Page Load....288

Real-Time WebSocket Communication and Dynamic Graph Updates....292

Real-Time Chart Update Mechanism....298

6.3 Server Program Structure for VQA....299

6.3.1 Libraries and Required Resources....301

6.3.2 Adding Libraries....301

6.3.3 Declaring Global Variables....302

6.3.4 Emptying the Screenshot Folders....303

6.3.5 Creating a WebSocket Server....304

6.3.6 Listening for a Client Connection Request....304

Receiving Metadata and Site Data....306

Reconstructing and Cropping the Image....307

Querying the Servlet and Handling the Response....310

6.4 Servlet Program Structure for VQA....314

6.4.1 Creating a Java Servlet in Eclipse....316

6.4.2 Libraries and Required Resources....320

6.4.3 Adding Libraries....321

6.4.4 Adding Imports....323

6.4.5 Declaring Global Variables....324

6.4.6 Handling an HTTP POST Request from a Client....326

6.4.7 Extracting the LBP Features....335

LBP Pattern for Original Image....336

Binarization Process for Gaussian Blur....338

Binarization Process for White Noise....339

Binarization Process for JPEG Compression....340

Binarization Process for JPEG2000 Compression....341

6.4.8 Applying Distortions....342

Applying Gaussian Blur for Blurring....342

Applying White Noise for Noising....347

Applying JPEG Compression for Blocking....350

Applying JPEG2000 Compression for Ringing....352

6.4.9 Calculating the Similarity Index....355

6.4.10 Scaling the Similarity Scores....357

Implementing the Runner....357

Displaying Usage Information....365

File Handling and Buffer Management....366

Scaling Target and Attribute Values....367

6.4.11 Printing Utilities....369

6.4.12 Predicting the MOS....369

6.5 VQA Application Testing and Deployment....374

6.6 Summary....381

6.7 References – Chapter 6....382

Chapter 7: NTMA and VQA Integration....384

7.1 System Model for Integrated NTMA and VQA Application....384

7.1.1 Components and Functionalities....386

7.1.2 Prediction and Classification of Network Traffic with Video Quality Metrics....387

7.1.3 Integrated NTMA/VQA Application Layout....388

7.1.4 Client–Server–Servlet Interaction....390

7.2 Client Program Structure for Integrated NTMA/VQA Application....390

7.2.1 Configuring Extension Settings and Permissions....391

7.2.2 Configuring the Background Script....393

7.2.3 Building the User Interface....393

File Functionality....394

Libraries and Required Resources....395

Creating the Document Structure....396

Adding the Document Details and References to External Resources....396

Adding the Toggle, Graphs, and Dashboard Components....397

Styling the Components through Internal CSS....403

Adding the User Interface to Google Chrome....409

Visualizing the User Interface....410

7.2.4 Building the Client Script....411

File Functionality....411

Creating the Script Structure....412

Initializing and Configuring the Charts on Page Load....413

Real-Time WebSocket Communication and Dynamic Graph Updates....421

Real-Time Chart Update Mechanism....430

7.3 Server Program Structure for Integrated NTMA/VQA Application....431

7.3.1 Libraries and Required Resources....432

7.3.2 Adding Libraries....433

7.3.3 Declaring Global Variables....434

7.3.4 Emptying the Screenshot Folders....436

7.3.5 Fetching the Local Databases....436

7.3.6 Creating a WebSocket Server....437

7.3.7 Listening for a Client Connection....437

Adding the VQA Block....438

Adding the NTMA Block....446

7.3.8 Prediction, Classification, and Network Score Computation Methods....451

7.4 Integrated NTMA/VQA Application Testing and Deployment....451

7.5 Summary....458

7.6 References–Chapter 7....459

Index....461

This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various devices and web browsers.

The book begins by delving into NTMA, explaining fundamental concepts and providing an overview of existing applications and research within this domain. It also goes into the essentials of VQA and offers a survey of the latest developments in VQA algorithms. The book includes a thorough examination of machine learning algorithms that find application in both NTMA and VQA, with a specific emphasis on classification and prediction algorithms such as the Multi-Layer Perceptron and Support Vector Machine. The book also explores the software architecture of the NTMA client-server application. This architecture is meticulously developed using HTML, CSS, Node.js, and JavaScript. Practical aspects of developing the Video Quality Assessment (VQA) model using JavaScript and Java are presented. Lastly, the book provides detailed guidance on implementing a complete system model that seamlessly merges NTMA and VQA into a unified web application, all built upon a client-server paradigm.

By the end of the book, you will understand NTMA and VQA concepts and will be able to apply machine learning to both domains and develop and deploy your own NTMA and VQA applications using JavaScript and Node.js.

What You Will Learn

• What are the fundamental concepts, existing applications, and research on NTMA?
• What are the existing software and current research trends in VQA?
• Which machine learning algorithms are used in NTMA and VQA?
• How do you develop NTMA and VQA web-based applications using JavaScript, HTML, and Node.js?

Who This Book Is For

Software professionals and machine learning engineers involved in the fields of networking and telecommunications