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El. knyga: Malware Diffusion Models for Modern Complex Networks: Theory and Applications

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(School of Electronic Engineering and Computer Science, Queen Mary University of London, U.K.), (School of Electrical and Computer Engineering, National Technical University of Athens, Greece)
  • Formatas: EPUB+DRM
  • Išleidimo metai: 02-Feb-2016
  • Leidėjas: Morgan Kaufmann Publishers In
  • Kalba: eng
  • ISBN-13: 9780128027165
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Malware Diffusion Models for Modern Complex Networks: Theory and ApplicationsDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Išleidimo metai: 02-Feb-2016
  • Leidėjas: Morgan Kaufmann Publishers In
  • Kalba: eng
  • ISBN-13: 9780128027165
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Malware Diffusion Models for Wireless Complex Networks: Theory and Applications provides a timely update on malicious software (malware), a serious concern for all types of network users, from laymen to experienced administrators. As the proliferation of portable devices, namely smartphones and tablets, and their increased capabilities, has propelled the intensity of malware spreading and increased its consequences in social life and the global economy, this book provides the theoretical aspect of malware dissemination, also presenting modeling approaches that describe the behavior and dynamics of malware diffusion in various types of wireless complex networks.

Sections include a systematic introduction to malware diffusion processes in computer and communications networks, an analysis of the latest state-of-the-art malware diffusion modeling frameworks, such as queuing-based techniques, calculus of variations based techniques, and game theory based techniques, also demonstrating how the methodologies can be used for modeling in more general applications and practical scenarios.

  • Presents a timely update on malicious software (malware), a serious concern for all types of network users, from laymen to experienced administrators
  • Systematically introduces malware diffusion processes, providing the relevant mathematical background
  • Discusses malware modeling frameworks and how to apply them to complex wireless networks
  • Provides guidelines and directions for extending the corresponding theories in other application domains, demonstrating such possibility by using application models in information dissemination scenarios

Daugiau informacijos

This book provides a comprehensive reference on malware diffusion models, showing how the proliferation of smartphones and tablets has precipitated the need for an increased awareness of malicious content, a serious concern for all types of network users, from laymen to experienced administrators.
Preface xi
PART 1 MALWARE DIFFUSION MODELING FRAMEWORK
Chapter 1 Fundamentals of Complex Communications Networks
3(24)
1.1 Introduction to Communications Networks and Malicious Software
3(2)
1.2 A Brief History of Communications Networks and Malicious Software
5(10)
1.2.1 From Computer to Communications Networks
5(4)
1.2.2 The Emergence and Proliferation of Wireless Networks
9(3)
1.2.3 Malicious Software and the Internet
12(3)
1.3 Complex Networks and Network Science
15(12)
1.3.1 Complex Networks
16(5)
1.3.2 Network Science
21(2)
1.3.3 Network Graphs Primer
23(4)
Chapter 2 Malware Diffusion in Wired and Wireless Complex Networks
27(12)
2.1 Diffusion Processes and Malware Diffusion
27(3)
2.1.1 General Diffusion Processes
27(1)
2.1.2 Diffusion of Malware in Communication Networks
28(2)
2.2 Types of Malware Outbreaks in Complex Networks
30(4)
2.3 Node Infection Models
34(5)
Chapter 3 Early Malware Diffusion Modeling Methodologies
39(24)
3.1 Introduction
39(1)
3.2 Basic Epidemics Models
39(14)
3.2.1 Simple (Classical) Epidemic Model---SI Model
41(3)
3.2.2 General Epidemic Model: Kermack-McKendrick Model
44(2)
3.2.3 Two-factor Model
46(3)
3.2.4 Dynamic Quarantine
49(4)
3.3 Other Epidemics Models
53(5)
3.3.1 Epidemics Model in Scale-free Networks
53(2)
3.3.2 Generalized Epidemics-Endemics Models
55(3)
3.4 Miscellaneous Malware Modeling Models
58(1)
3.5 Scope and Achievements of Epidemics
59(4)
PART 2 STATE-OF-THE-ART MALWARE MODELING FRAMEWORKS
Chapter 4 Queuing-based Malware Diffusion Modeling
63(44)
4.1 Introduction
63(1)
4.2 Malware Diffusion Behavior and Modeling via Queuing Techniques
64(3)
4.2.1 Basic Assumptions
64(2)
4.2.2 Mapping of Malware Diffusion to a Queuing Problem
66(1)
4.3 Malware Diffusion Modeling in Nondynamic Networks
67(24)
4.3.1 Evaluation Metrics
71(1)
4.3.2 Steady-state Behavior and Analysis
72(19)
4.4 Malware Diffusion Modeling in Dynamic Networks with Churn
91(16)
4.4.1 Malware Diffusion Models and Network Churn
94(1)
4.4.2 Open Queuing Network Theory for Modeling Malware Spreading in Complex Networks with Churn
94(4)
4.4.3 Analysis of Malware Propagation in Networks with Churn
98(3)
4.4.4 Demonstration of Queuing Framework for Malware Spreading in Complex and Wireless Networks
101(6)
Chapter 5 Malware-Propagative Markov Random Fields
107(32)
5.1 Introduction
107(1)
5.2 Markov Random Fields Background
108(7)
5.2.1 Markov Random Fields
108(2)
5.2.2 Gibbs Distribution and Relation to MRFs
110(1)
5.2.3 Gibbs Sampling and Simulated Annealing
111(4)
5.3 Malware Diffusion Modeling Based on MRFs
115(3)
5.4 Regular Networks
118(9)
5.4.1 Chain Networks
119(5)
5.4.2 Regular Lattices: Finite and Infinite Grids
124(3)
5.5 Complex Networks with Stochastic Topologies
127(12)
5.5.1 Random Networks
129(2)
5.5.2 Small-world Networks
131(1)
5.5.3 Scale-free Networks
132(1)
5.5.4 Random Geometric Networks
133(1)
5.5.5 Comparison of Malware Diffusion in Complex Topologies
134(5)
Chapter 6 Optimal Control Based Techniques
139(16)
6.1 Introduction
139(3)
6.2 Example---an Optimal Dynamic Attack: Seek and Destroy
142(4)
6.2.1 Dynamics of State Evolution
143(2)
6.2.2 Objective Functional
145(1)
6.3 Worm's Optimal Control
146(9)
6.3.1 Structure of the Maximum Damage Attack
148(3)
6.3.2 Proof of Theorem 6.1
151(1)
6.3.3 Proof of Theorem 6.1: Optimal Rate of Killing
152(2)
Summary
154(1)
Chapter 7 Game-Theoretic Techniques
155(14)
7.1 Introduction
155(2)
7.2 System Model
157(3)
7.3 Network-Malware Dynamic Game
160(9)
7.3.1 Formulation
160(1)
7.3.2 A Framework for Computation of the Saddle-point Strategies
161(2)
7.3.3 Structural Properties of Saddle-point Defense Strategy
163(3)
7.3.4 Structure of the Saddle-point Attack Strategy
166(1)
Summary
167(2)
Chapter 8 Qualitative Comparison
169(12)
8.1 Introduction
169(1)
8.2 Computational Complexity Comparison
170(2)
8.3 Implementation Efficiency Comparison
172(1)
8.4 Sensitivity Comparison
173(1)
8.5 Practical Value Comparison
174(2)
8.6 Modeling Differences
176(1)
8.7 Overall Comparison
177(4)
PART 3 APPLICATIONS AND THE ROAD AHEAD
Chapter 9 Applications of State-of-the-art Malware Modeling Frameworks
181(34)
9.1 Network Robustness
181(11)
9.1.1 Introduction and Objectives
181(1)
9.1.2 Queuing Model for the Aggregated Network Behavior under Attack
181(1)
9.1.3 Steady-state Behavior and Analysis
182(3)
9.1.4 Optimal Attack Strategies
185(2)
9.1.5 Robustness Analysis for Wireless Multihop Networks
187(4)
9.1.6 Conclusions
191(1)
9.2 Dynamics of Information Dissemination
192(17)
9.2.1 Introduction to Information Dissemination
192(3)
9.2.2 Previous Works on Information Dissemination
195(1)
9.2.3 Epidemic-based Modeling Framework for IDD in Wireless Complex Communication Networks
196(2)
9.2.4 Wireless Complex Networks Analyzed and Assessment Metrics
198(3)
9.2.5 Useful-information Dissemination Epidemic Modeling
201(8)
9.3 Malicious-information Propagation Modeling
209(6)
9.3.1 SIS Closed Queuing Network Model
210(5)
Chapter 10 The Road Ahead
215(8)
10.1 Introduction
215(1)
10.2 Open Problems for Queuing-based Approaches
215(2)
10.3 Open Problems for MRF-based Approaches
217(1)
10.4 Optimal Control and Dynamic Game Frameworks
218(1)
10.5 Open Problems for Applications of Malware Diffusion Modeling Frameworks
219(1)
10.6 General Directions for Future Work
220(3)
Chapter 11 Conclusions
223(6)
11.1 Lessons Learned
223(3)
11.2 Final Conclusions
226(3)
PART 4 APPENDICES
APPENDIX A Systems of Ordinary Differential Equations
229(6)
A.1 Initial Definitions
229(1)
A.2 First-order Differential Equations
230(1)
A.3 Existence and Uniqueness of a Solution
231(1)
A.4 Linear Ordinary Differential Equations
232(1)
A.5 Stability
233(2)
APPENDIX B Elements of Queuing Theory and Queuing Networks
235(20)
B.1 Introduction
235(1)
B.2 Basic Queuing Systems, Notation, and Little's Law
235(1)
B.2.1 Elements of a Queuing System
236(1)
B.2.2 Fundamental Notation and Quantities of Interest
237(1)
B.2.3 Relation Between Arrival-Departure Processes and Little's Law
238(2)
B.3 Markovian Systems in Equilibrium
240(1)
B.3.1 Discrete-time Markov Chains
240(2)
B.3.2 Continuous-time Markov Processes
242(1)
B.3.3 Birth-and-Death Processes
242(2)
B.3.4 The M/M/1 Queuing System
244(1)
B.3.5 The M/M/m System and Other Multiserver Queuing Systems
244(3)
B.4 Reversibility
247(1)
B.5 Queues in Tandem
248(2)
B.6 Queuing Networks
250(2)
B.6.1 Analytical Solution of Two-queue Closed Queuing Network
252(3)
APPENDIX C Optimal Control Theory and Hamiltonians
255(28)
C.1 Basic Definitions, State Equation Representations, and Basic Types of Optimal Control Problems
255(4)
C.2 Calculus of Variations
259(2)
C.3 Finding Trajectories that Minimize Performance Measures
261(1)
C.3.1 Functionals of a Single Function
261(1)
C.3.2 Functionals of Several Independent Functions
262(1)
C.3.3 Piecewise-smooth Extremals
263(1)
C.3.4 Constrained Extrema
263(3)
C.4 Variational Approach for Optimal Control Problems
266(1)
C.4.1 Necessary Conditions for Optimal Control
266(1)
C.4.2 Pontryagin's Minimum Principle
267(1)
C.4.3 Minimum-time Problems
268(1)
C.4.4 Minimum Control-effort Problems
269(2)
C.4.5 Singular Intervals in Optimal Control Problems
271(1)
C.5 Numerical Determination of Optimal Trajectories
272(1)
C.5.1 Steepest Descent
273(1)
C.5.2 Variation of Extremals
274(1)
C.5.3 Quasilinearization
275(1)
C.5.4 Gradient Projection
276(3)
C.6 Relationship Between Dynamic Programming (DP) and Minimum Principle
279(4)
Bibliography 283(10)
Author Index 293(6)
Subject Index 299
Vasileios Karyotis received his Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece, in June 2004, his M.Sc. degree in Electrical Engineering from the University of Pennsylvania, PA, USA, in August 2005 and his Ph.D. in Electrical and Computer Engineering from NTUA, Greece, in June 2009. Since July 2009 he is with the Network Management andOptimal Design (NETMODE) Lab of NTUA, Greece, where he is currently a senior researcher.

Dr. Karyotis was awarded a fellowship from the Department of Electrical and Systems Engineering of the University of Pennsylvania (2004-2005) and one of two departmental fellowships for exceptional graduate students from the School of Electrical and Computer Engineering of NTUA (2007-2009). His research interests span the areas of stochastic modeling and performance evaluation of communications networks, resource allocation, malware propagation and network science.

He has given various tutorial presentations in conferences, workshops and seminars, and he has been a TPC co-chair of the 2014 IEEE INFOCOM workshop on Dynamic Social Networks (DySON) and the 2015 IEEE ICC workshop on Dynamic Social Networks (DySON). He is a member of the Technical Chamber of Greece since 2004, and a member of the IEEE since 2003. He has participated in various R&D projects funded by the EC (FP6, FP7), the European Space Agency (ESA), and the Greek General Secretariat for Research and Technology (GSRT).

MHR. Khouzani received a B.Sc. degree in Electrical Engineering from Sharif University of Technology in 2006. He subsequently joined the University of Pennsylvania (UPenn) with a fellowship award. He received his Ph.D. in Electrical and Systems Engineering in 2011 with the best dissertation award among his graduation class. He has since held postdoctoral research positions with the Ohio State University (OSU), the University of Southern California (USC), Royal Holloway, University of London (RHUL), and most recently, Queen Mary, University of London (QMUL). Dr. Khouzani's research is in the area of communication networks and cyber-security. He uses diverse analytical tools from areas such as probability, statistics, control theory, optimization, and decision and game theory, to contribute to the emerging field of the "science of security".
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