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Multi-Fractal Traffic and Anomaly Detection in Computer Communications [Kõva köide]

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  • Formaat: Hardback, 282 pages, kõrgus x laius: 254x178 mm, kaal: 453 g, 13 Tables, black and white; 81 Line drawings, black and white; 15 Halftones, black and white; 96 Illustrations, black and white
  • Ilmumisaeg: 29-Dec-2022
  • Kirjastus: CRC Press
  • ISBN-10: 1032408464
  • ISBN-13: 9781032408460
Teised raamatud teemal:
Multi-Fractal Traffic and Anomaly Detection in Computer CommunicationsSuurem pilt
  • Formaat: Hardback, 282 pages, kõrgus x laius: 254x178 mm, kaal: 453 g, 13 Tables, black and white; 81 Line drawings, black and white; 15 Halftones, black and white; 96 Illustrations, black and white
  • Ilmumisaeg: 29-Dec-2022
  • Kirjastus: CRC Press
  • ISBN-10: 1032408464
  • ISBN-13: 9781032408460
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032408460.html
Märksõnad:
This book provides a comprehensive theory of mono- and multi-fractal traffic, including the basics of long-range dependent time series and 1/f noise, ergodicity and predictability of traffic, traffic modeling and simulation, stationarity tests of traffic, traffic measurement and the anomaly detection of traffic in communications networks.

Proving that mono-fractal LRD time series is ergodic, the book exhibits that LRD traffic is stationary. The author shows that the stationarity of multi-fractal traffic relies on observation time scales, and proposes multi-fractional generalized Cauchy processes and modified multi-fractional Gaussian noise. The book also establishes a set of guidelines for determining the record length of traffic in measurement. Moreover, it presents an approach of traffic simulation, as well as the anomaly detection of traffic under distributed-denial-of service attacks.

Scholars and graduates studying network traffic in computer science will find the book beneficial.
Preface xi
Acknowledgments xiii
Part I Fundamentals
Chapter 1 Fractal Time Series
3(26)
1.1 Background
3(1)
1.2 Fractal Time Series: A View From Fractional Systems
4(4)
1.3 Basic Properties Of Fractal Time Series
8(3)
1.4 Some Models Of Fractal Time Series
11(12)
1.5 Summary
23(6)
References
24(5)
Chapter 2 On 1/f Noise
29(24)
2.1 Introduction
29(1)
2.2 Preliminaries
30(4)
2.3 Hyperbolically Decayed Acfs And 1/F Noise
34(3)
2.4 Heavy-Tailed Pdfs And 1/F Noise
37(3)
2.5 Fractionally Generalized Langevin Equation And 1/F Noise
40(3)
2.6 Summary
43(10)
References
43(10)
Chapter 3 Power Laws of Fractal Data in Cyber-Physical Networking Systems
53(14)
3.1 Background
53(2)
3.2 Biref Of Power Laws
55(3)
3.3 Cases Of Power Laws In Cpns
58(3)
3.4 Some Equations For Power-Law-Type Data
61(1)
3.5 Summary
62(5)
References
62(5)
Chapter 4 Ergodicity of Long-Range-Dependent Traffic
67(16)
4.1 Background
67(1)
4.2 Preliminaries
68(4)
4.3 Problem Statements
72(1)
4.4 Results
73(6)
4.5 Discussions And Summary
79(4)
References
80(3)
Chapter 5 Predictability of Long-Range-Dependent Series
83(12)
5.1 Introduction
83(1)
5.2 Problem Statements
84(2)
5.3 Predictability Of Lrd Series
86(2)
5.4 Summary
88(7)
References
88(7)
Part II Traffic Modeling and Traffic Data Processing
Chapter 6 Long-Range Dependence and Self-Similarity of Daily Traffic with Different Protocols
95(22)
6.1 Background
95(3)
6.2 Data
98(1)
6.3 Preliminaries: Brief Of Generalized Cauchy Process
99(2)
6.4 Modeling Results
101(9)
6.5 Discussions
110(1)
6.6 Summary
111(6)
References
111(6)
Chapter 7 Stationarity Test of Traffic
117(20)
7.1 Background
117(2)
7.2 Correlation Method For Stationarity Test Of Lrd Traffic
119(1)
7.3 Case Study
120(7)
7.4 Discussions
127(7)
7.5 Summary
134(3)
References
134(3)
Chapter 8 Record Length Requirement of LRD Traffic
137(36)
8.1 Background And Problem Statements
137(4)
8.2 Theoretical Results
141(6)
8.3 Practical Considerations
147(4)
8.4 Case Study
151(8)
8.5 Discussions
159(2)
8.6 Summary
161(12)
References
162(11)
Part III Multi-fractal Models of Traffic
Chapter 9 Multi-Fractional Generalized Cauchy Process and Its Application to Traffic
173(20)
9.1 Introduction
173(3)
9.2 The MGC Process
176(3)
9.3 PSD Of The MGC Process
179(2)
9.4 Computations Of D(T) And H(T)
181(1)
9.5 Case Study
182(6)
9.6 Discussions
188(1)
9.7 Summary
189(4)
References
189(4)
Chapter 10 Modified Multi-fractional Gaussian Noise and Its Application to Traffic
193(20)
10.1 Introduction
193(4)
10.2 Modified Multi-Fractional Guassian Noise
197(5)
10.3 On Stationarity Of MMFGN
202(3)
10.4 Application To Stationarity Test Of Traffic
205(2)
10.5 Summary
207(6)
References
208(5)
Chapter 11 Traffic Simulation
213(26)
11.1 Introduction
213(1)
11.2 Simulations Based On Given PDF/PSD/ACF
214(5)
11.3 Generation Of LRD Traffic Of GC Type
219(10)
11.4 Discussions
229(3)
11.5 Summary
232(7)
References
232(7)
Part IV Anomaly Detection of Traffic
Chapter 12 Reliably Identifying Signs of DDOS Flood Attacks Based on Traffic Pattern Recognition
239(14)
12.1 Background
240(1)
12.2 Feature Extraction
241(3)
12.3 Identification Decision
244(5)
12.4 Case Study
249(2)
12.5 Discussions and Summary
251(2)
References
251(2)
Chapter 13 Change Trend of Hurst Parameter of Multi-Scale Traffic under DDOS Flood Attacks
253(10)
13.1 Background
253(2)
13.2 Test Data
255(1)
13.3 Brief Of Data Traffic
255(2)
13.4 Using H To Describe Abnormality Of Traffic Under Ddos Flood Attacks
257(1)
13.5 Change Trend Of H
258(2)
13.6 Summary
260(3)
References
260(3)
Chapter 14 Postscript
263(2)
14.1 Local Versus Global Of Fractal Traffic
263(1)
14.2 Stationarity Versus Multi-Fractal Property Of Traffic
264(1)
14.3 Open Problems
264(1)
References 265(2)
Appendix 267(14)
Index 281
Ming Li, PhD, is a professor at Ocean College, Zhejiang University and the East China Normal University. He has been a contributor for many years to the fields of computer science, mathematics, statistics, and mechanics. He has authored more than 200 articles and 5 monographs on the subjects.