Security, privacy and reliability in computer communications and networks /

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Future communication networks aim to build an intelligent and efficient living environment by connecting a variety of heterogeneous networks to fulfill complicated tasks. These communication networks bring significant challenges in building secure and reliable communication networks to address the n...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Sha, Kewei (Editor ), Striegel, Aaron (Editor ), Song, Min (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Gistrup, Denmark : River Publishers, [2017]
Colección:River Publishers series in communications.
Temas:
Computer networks > Security measures.
Réseaux d'ordinateurs > Sécurité > Mesures.
COMPUTERS > Security > General.
SCIENCE / Energy
TECHNOLOGY / Telecommunications
Computer networks > Security measures
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Preface xv
  • Acknowledgments xvii
  • List of Contributors xix
  • List of Figures xxiii
  • List of Tables xxxi
  • List of Algorithms xxxv
  • List of Abbreviations xxxvii PART I: Privacy 1 Distributed Beamforming Relay Selection to Increase Base Station Anonymity inWireless Ad Hoc Networks 3 Jon R.Ward and Mohamed Younis 1.1 Introduction 4
  • 1.2 Anonymity Definition, Metrics, and Contemporary Measures 8
  • 1.2.1 Anonymity Definition and Assessment 8
  • 1.2.2 Antitraffic Analysis Measures 11
  • 1.3 System Assumptions and Attack Model 13
  • 1.3.1 Network Model 14
  • 1.3.2 Adversary Model 15
  • 1.3.3 Evidence Theory and Belief Metric 16
  • 1.4 Distributed Beamforming to Increase the BS Anonymity 21
  • 1.4.1 Overview of the DiBAN Protocol 21
  • 1.4.2 DiBAN Illustrative Example 25
  • 1.4.3 DiBAN Energy Analysis 26
  • 1.5 Distributed Beamforming Relay Selection Approach 28
  • 1.6 Validation Experiments 32
  • 1.6.1 Simulation Environment 32
  • 1.6.2 Simulation Results 32
  • 1.7 Conclusions and FutureWork 38 Appendix I: Numerical Evidence Theory Belief Calculation Example 39
  • References 42
  • 2 Privacy-Preserving and Efficient Information Sharing Scheme for VANET Secure Communication 49 Cong Guo, Liehuang Zhu and Zijian Zhang 2.1 Introduction 50
  • 2.2 RelatedWorks 52
  • 2.3 System Model and Preliminaries 54
  • 2.3.1 Network Model 54
  • 2.3.2 Attack Model 55
  • 2.3.3 Security Requirements 55
  • 2.4 The Proposed PETS Scheme 56
  • 2.4.1 Scheme Overview 56
  • 2.4.2 System Initiation 56
  • 2.4.3 Vehicle-RSU Key Agreement 58
  • 2.4.4 Traffic Information Collection and Aggregation 59
  • 2.4.5 Traffic Jam Message Propagation 61
  • 2.5 Security Analysis 63
  • 2.6 Performance Evaluation 64
  • 2.6.1 Traffic Information Sending/Collection Overhead 65
  • 2.6.2 Traffic Information Propagation/Verification Overhead 66
  • 2.6.3 Scheme Simulation 68
  • 2.7 Conclusion 74
  • References 74 PART II: Vulnerabilities, Detection and Monitoring 3 DIAMoND: Distributed Intrusion/Anomaly Monitoring for Nonparametric Detection 79 Maciej Korczyńnski, Ali Hamieh, Jun Ho Huh, Henrik Holm, S. Raj Rajagopalan and Nina H. Fefferman 3.1 Introduction 79.
  • 249 8.5.3 Optimized Method II 252 8.6 Implementation Results 253 8.7 Conclusions 256 Acknowledgments 256 References 257 9 Multi-antenna Transmission Technique with Constellation Shaping for Secrecy at Physical Layer 259 Paulo Montezuma and Rui Dinis 9.1 Introduction 260 9.2 Transmitter Structure 261 9.3 Transmitter Configuration Possibilities and Security 263 9.4 Receivers and the Impact of Information Directivity 268 9.4.1 Simulation Results 271 9.4.2 Transmitter Configuration Effects in MI and Secrecy 275 9.5 Conclusions 281 Acknowledgments 282 References 282 PART VI: Reliable System Design 10 Active Sub-Areas-Based Multi-Copy Routing in VDTNs 287 BoWu, Haiying Shen and Kang Chen 10.1 Introduction 288 10.2 RelatedWork 291 10.3 Identification of Each Vehicle's Active Sub-areas 292 10.4 Trace Measurement 294 10.4.1 Vehicle Mobility Pattern 295 10.4.2 Relationship between Contact and Location 297 10.5 Active Area-based Routing Method 298 10.5.1 Traffic-Considered Shortest Path Spreading 299 10.5.1.1 Road traffic measurement 300 10.5.1.2 Building traffic-considered shortest path tree 300 10.5.2 Contact-based Scanning in Each Active Sub-area 301 10.5.2.1 Maintaining scanning history table 302 10.5.2.2 Routing algorithm in sub-area 302 10.5.3 Distributed Active Sub-area Updates 304 10.5.3.1 Building the active sub-area information table 304 10.5.3.2 Maintaining the active sub-area information table 305 10.6 Performance Evaluation 305 10.6.1 Performance with Different Number of Copies 306 10.6.2 Performance with Different Memory Sizes 308 10.6.3 Performance of Distributed AAR (DAAR) 309 10.7 Conclusion 312 Acknowledgments 312 References 312 11 RobustGeo: Disruption-Tolerant Geo-Routing Protocol 315 Ruolin Fan, Yu-Ting Yu and Mario Gerla 11.1 Introduction 316 11.2 Background 318 11.2.1 Location-based Routing Algorithms 318 11.2.2 Delay-Tolerant Networks 319 11.3 Design 321 11.3.1 Geo-Routing 321 11.3.2 Disruption Tolerance 321 11.3.2.1 Perimeter forwarding with packet replication 322 11.3.2.2 Single-hop broadcasting to explore multiple paths 323 11.3.2.3 Scheduling 325 11.4 Analysis 326 11.5 Evaluation 330 11.6 RelatedWork 337 11.7 Conclusion and FutureWork 338 References 339 12 Social Similarity-based Multicast Framework in Opportunistic Mobile Social Networks 343 Xiao Chen, Yuan Xu, Suho Oh 12.1 Introduction 344 12.2 RelatedWorks 346 12.3 Preliminary 347 12.3.1 Definition of Static Social Features 347 12.3.2 Definitions of Dynamic Social Features 347 12.3.2.1 Dynamic social features 348 12.3.2.2 Enhanced dynamic social features 348 12.3.3 Calculation of Social Similarity 349 12.4 Multicast Routing Protocols 350 12.4.1 Social Similarity-based Multicast Framework 350 12.5 Analysis 352 12.5.1 Property of Dynamic Social Feature Definition (12.2) 352 12.5.2 The Number of Forwardings 353 12.5.3 The Number of Copies 356 12.6 Simulations 356 12.6.1 Algorithms Compared 357 12.6.2 Evaluation Metrics 357 12.6.3 Simulation Setup 357 12.6.4 Simulation Results 358 12.7 Conclusion 360 Acknowledgements 361 References 361 13 Ensuring QoS for IEEE 802.11 Real-Time Communications Using an AIFSN Prediction Scheme 365 Estefan#x83;ia Coronado, Josée Villalóon and Antonio Garrido 13.1 Introduction 366 13.2 QoS in IEEE 802.11 Networks 367 13.2.1 IEEE 802.11e 367 13.2.2 Dynamic Adaptation in IEEE 802.11e 369 13.3 Supervised Learning 370 13.3.1 J48 Decision Tree Classifier 371 13.3.2 M5Rules 372 13.4 AIFSN Tuning Scheme 374 13.4.1 Proposal Description 374 13.4.2 Design of the Predictive Models 378 13.5 Performance Evaluation 382 13.6 Conclusions 390 Acknowledgments 391 References 391 Index 395.
  • 3.2 Literature Review 83
  • 3.3 System Design 86
  • 3.3.1 Architecture Overview 86
  • 3.3.2 Detection Unit 87
  • 3.3.3 Coordination Unit 87
  • 3.3.4 Communication Protocol 90
  • 3.3.5 Neighborhood Strategies 90
  • 3.3.6 Rogue Nodes 92
  • 3.4 Evaluation Setup 92
  • 3.4.1 Software Implementation 92
  • 3.4.2 Physical Topologies 93
  • 3.4.3 Legitimate and Malicious Traffic 93
  • 3.5 Emulation Results 94
  • 3.5.1 Detection Accuracy 94
  • 3.5.2 Impact of Physical Topologies 95
  • 3.5.3 Influence of Neighborhood Strategies 99
  • 3.5.4 Minimal and Marginal Deployment Gain 99
  • 3.6 Conclusions 101 Acknowledgments 102
  • References 102
  • 4 Detection of Service Level Agreement (SLA) Violations in Memory Management in Virtual Machines 107 Xiongwei Xie,WeichaoWang and Tuanfa Qin 4.1 Introduction 108
  • 4.2 RelatedWork 110
  • 4.2.1 Information Leakage among Virtual Machines 110
  • 4.2.2 Service Level Agreement Enforcement 111
  • 4.3 The Proposed Approaches 112
  • 4.3.1 Memory Overcommitment in Virtualization Environments 112
  • 4.3.2 Memory Deduplication in VM Hypervisors 113
  • 4.3.3 System Assumptions 115
  • 4.3.4 Basic Ideas of the Proposed Approaches 115
  • 4.3.5 Details of Implementation 117
  • 4.3.5.1 Choice of memory pages 117
  • 4.3.5.2 Measurement of access time 118
  • 4.3.5.3 Verification of memory access order 119
  • 4.3.6 Detection Procedures of the SLAViolations 120
  • 4.4 Experimental Results 123
  • 4.4.1 Experimental Environment Setup 123
  • 4.4.2 Experiments and Results 123
  • 4.4.3 Impacts on System Performance 127
  • 4.5 Discussion 129
  • 4.5.1 Reducing False Alarms 129
  • 4.5.2 Impacts of Extra Memory Demand 131
  • 4.5.3 Building Unified Detection Algorithm 131
  • 4.6 Conclusion 132
  • References 132
  • 5 Analysis of Mobile Threats and Security Vulnerabilities for Mobile Platforms and Devices 139 Syed Rizvi, Gabriel Labrador, Whitney Hernandez and Kelsey Karpinski 5.1 Introduction 140
  • 5.2 Analysis of Mobile Platforms 141
  • 5.2.1 Dominating Mobile Platforms 142.
  • 5.2.1.1 iPhone Operating System (iOS) 142
  • 5.2.1.2 Android operating system (Android) 143
  • 5.2.1.3 BlackBerry operating system 144
  • 5.2.2 Security Models for Mobile Platforms 144
  • 5.2.2.1 iOS security model 145
  • 5.2.2.2 Android security model 145
  • 5.2.2.3 BlackBerry security model 146
  • 5.2.3 Existing Security Vulnerabilities in Mobile Platforms 147
  • 5.2.3.1 Potential vulnerabilities 147
  • 5.2.3.2 Mobile device malware 148
  • 5.3 Threat Model for Mobile Platforms 149
  • 5.3.1 Goals and Motives for an Attacker 150
  • 5.3.1.1 Cybercriminals: outsourcing sensitive data 150
  • 5.3.1.2 Cybercriminals: cyber heist 150
  • 5.3.1.3 Cybercriminals: corporate espionage and sabotage 151
  • 5.3.2 Attack Vectors or Modern Exploitation Techniques for Mobile Devices 152
  • 5.3.2.1 Susceptibility on the mobile through hardware 152
  • 5.3.2.2 Attacking through theWeb 153
  • 5.3.2.3 Mobile intrusion and deception through social engineering 153
  • 5.3.2.4 Attacking through the mobile network 154
  • 5.3.2.5 Cyber Arson through common mobile applications 155
  • 5.3.2.6 Attacking via Bluetooth connection 155
  • 5.3.3 Types of Malwares in Mobile Devices 156
  • 5.3.3.1 Trojan-related malware 156
  • 5.3.3.2 Worms targeting mobile devices 156
  • 5.3.3.3 Viruses on the mobile 157
  • 5.3.3.4 Ransomware: mobile kidnapping 157
  • 5.3.3.5 Mobile botnets 158
  • 5.4 Defense Mechanisms for Securing Mobile Platforms 158
  • 5.4.1 Keychain Authentication and Encryption 158
  • 5.4.2 Binary Protection and Hardening 159
  • 5.4.3 Third-Party OS Products 160
  • 5.4.4 Obfuscators and Optimizers 161
  • 5.4.5 Compiler and Linker Defense Mechanisms 161
  • 5.4.6 Certificate-based Mobile Authentication 162
  • 5.4.7 Token-based Mobile Authentication 162
  • 5.4.8 Summary 163
  • 5.5 RelatedWork 163
  • 5.6 Threats Analysis and Future Trends 167
  • 5.7 Conclusion 168
  • References 168 PART III: Cryptographic Algorithms 6 Quasigroup-Based Encryption for Low-Powered Devices 177 Abhishek Parakh,William Mahoney, Leonora Gerlock and Matthew Battey 6.1 Introduction 178.
  • 6.2 Background-Low Energy Cryptosystems 179
  • 6.3 Overview of Quasigroup Encryption 180
  • 6.4 The Preliminary Block Cipher Design 181
  • 6.5 Overview of Software Implementation 181
  • 6.6 Overview of Three FPGA Implementations 182
  • 6.6.1 The Quasigroup Implementation 183
  • 6.6.2 Comparison Design-Parallel AES 183
  • 6.6.3 Hybrid Front-End/AES Design 184
  • 6.7 Experimental Results 184
  • 6.8 Toward Single-Chip Implementation 186
  • 6.9 Algorithm Results for = to . 186
  • 6.10 Generating Quasigroups Fast 187
  • 6.11 Our Quasigroup Block Cipher Algorithm 190
  • 6.12 Cryptanalysis and Improvements in the Block Cipher 192
  • 6.13 Overview of General Linear Cryptanalytical Attack 192
  • 6.14 The LAT Design 194
  • 6.15 Pilingup Attempts forN=16, 32, and64
  • 196
  • 6.16 Analysis of the Attack on the Quasigroup 197
  • 6.17 The Issue of Total Linear Bias of 1/2
  • 198
  • 6.18 Attack Complexity 198
  • 6.19 Possible Changes that Could Be Made in the Design of This Attack Model 199
  • 6.20 Which Quasigroup Order Is Best? 199
  • 6.21 Conclusions 200
  • References 201
  • 7 Measuring Interpretation and Evaluation of Client-side Encryption Tools in Cloud Computing 205 Md. Alam Hossain, Ahsan-Ul-Ambia, Md. Al-Amin and Rahamatullah Khondoker 7.1 Introduction 206
  • 7.2 Cloud Service Providers (CSPs) 207
  • 7.3 Deployment Model of Cloud Service Provider 208
  • 7.4 Methodology 210
  • 7.5 Deriving the Attributes of Existing Tools 210
  • 7.5.1 AxCrypt 210
  • 7.5.2 nCrypted Cloud 212
  • 7.5.3 SafeBox 213
  • 7.5.4 SpiderOak 214
  • 7.5.5 Viivo 215
  • 7.6 Comparison of the Studied Tools 217
  • 7.7 Characteristics of the Studied Tools 217
  • 7.8 Security of Encryption and Key Generation Mechanisms of the Studied Tools 221
  • 7.9 Performance Measurement and Analysis 223
  • 7.9.1 System Setup 223
  • 7.9.1.1 Application tools 223
  • 7.9.1.2 Cloud service provider 223
  • 7.9.1.3 Testing environment 223
  • 7.9.2 Analysis 224
  • 7.10 Results and Discussion 228
  • 7.11 Conclusion and FutureWork 230.

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