Preface xiii
1 M2M in 5G Cellular Networks: Challenges, Proposed Solutions, and Future Directions 1
Kiran Ahuja and Indu Bala
1.1 Introduction 2
1.2 Literature Survey 5
1.3 Survey Challenges and Proposed Solutions of M2M 7
1.3.1 PARCH Overload Problem 8
1.3.2 Inefficient Radio Resource Utilization and Allocation 10
1.3.3 M2M Random Access Challenges 12
1.3.4 Clustering Techniques 13
1.3.5 QoS Provisioning for M2M Communications 15
1.3.6 Less Cost and Low Power Device Requirements 16
1.3.7 Security and Privacy 17
1.4 Conclusion 18
References 19
2 MAC Layer Protocol for Wireless Security 23
Sushmita Kumari and Manisha Bharti
2.1 Introduction 23
2.2 MAC Layer 24
2.2.1 Centralized Control 24
2.2.2 Deterministic Access 24
2.2.3 Non-Deterministic Access 24
2.3 Functions of the MAC Layer 25
2.4 MAC Layer Protocol 25
2.4.1 Random Access Protocol 26
2.4.2 Controlled Access Protocols 29
2.4.3 Channelization 31
2.5 MAC Address 31
2.6 Conclusion and Future Scope 33
References 33
3 Enhanced Image Security Through Hybrid Approach: Protect Your Copyright Over Digital Images 35
Shaifali M. Arora and Poonam Kadian
3.1 Introduction 36
3.2 Literature Review 38
3.3 Design Issues 40
3.3.1 Robustness Against Various Attack Conditions 40
3.3.2 Distortion and Visual Quality 41
3.3.3 Working Domain 42
3.3.4 Human Visual System (HVS) 43
3.3.5 The Trade-Off between Robustness and Imperceptibility 43
3.3.6 Computational Cost 43
3.4 A Secure Grayscale Image Watermarking Based on DWT-SVD 43
3.5 Experimental Results 45
3.6 Conclusion 52
References 52
4 Quantum Computing 59
Manisha Bharti and Tanvika Garg
4.1 Introduction 59
4.2 A Brief History of Quantum Computing 60
4.3 Postulate of Quantum Mechanics 61
4.4 Polarization and Entanglement 61
4.5 Applications and Advancements 63
4.5.1 Cryptography, Teleportation and Communication Networks 63
4.5.2 Quantum Computing and Memories 63
4.5.3 Satellite Communication Based on Quantum Computing 64
4.5.4 Machine Learning & Artificial Intelligence 65
4.6 Optical Quantum Computing 65
4.7 Experimental Realisation of Quantum Computer 66
4.7.1 Hetero-Polymers 66
4.7.2 Ion Traps 67
4.7.3 Quantum Electrodynamics Cavity 67
4.7.4 Quantum Dots 67
4.8 Challenges of Quantum Computing 67
4.9 Conclusion and Future Scope 68
References 68
5 Feature Engineering for Flow-Based IDS 69
Rahul B. Adhao and Vinod K. Pachghare
5.1 Introduction 70
5.1.1 Intrusion Detection System 71
5.1.2 IDS Classification 71
5.2 IP Flows 72
5.2.1 The Architecture of Flow-Based IDS 73
5.2.2 Wireless IDS Designed Using Flow-Based Approach 73
5.2.3 Comparison of Flow- and Packet-Based IDS 74
5.3 Feature Engineering 75
5.3.1 Curse of Dimensionality 76
5.3.2 Feature Selection 78
5.3.3 Feature Categorization 78
5.4 Classification of Feature Selection Technique 78
5.4.1 The Wrapper, Filter, and Embedded Feature Selection 78
5.4.2 Correlation, Consistency, and PCA-Based Feature Selection 80
5.4.3 Similarity, Information Theoretical, Sparse Learning, and Statistical-Based Feature Selection 80
5.4.4 Univariate and Multivariate Feature Selection 81
5.5 Tools and Library for Feature Selection 82
5.6 Literature Review on Feature Selection in Flow-Based IDS 82
5.7 Challenges and Future Scope 86
5.8 Conclusions 87
Acknowledgement 87
References 88
6 Environmental Aware Thermal (EAT) Routing Protocol for Wireless Sensor Networks 91
B. Banuselvasaraswathy and Vimalathithan Rathinasabapathy
6.1 Introduction 92
6.1.1 Single Path Routing Protocol 93
6.1.2 Multipath Routing Protocol 94
6.1.3 Environmental Influence on WSN 96
6.2 Motivation Behind the Work 97
6.3 Novelty of This Work 98
6.4 Related Works 99
6.5 Proposed Environmental Aware Thermal (EAT) Routing Protocol 102
6.5.1 Sensor Node Environmental Modeling and Analysis 104
6.5.2 Single Node Environmental Influence Modeling 105
6.5.3 Multiple Node Modeling 106
6.5.4 Sensor Node Surrounding Temperature Field 106
6.5.5 Sensor Node Remaining Energy Calculation 107
6.5.6 Delay Modeling 107
6.6 Simulation Parameters 108
6.7 Results and Discussion 109
6.7.1 Temperature Influence on Network 109
6.7.2 Power Consumption 109
6.7.3 Lifetime Analysis 110
6.7.4 Delay Analysis 111
6.8 Conclusion 112
References 112
7 A Comprehensive Study of Intrusion Detection and Prevention Systems 115
Bhoopesh Singh Bhati, Dikshita, Nitesh Singh Bhati and Garvit Chugh
7.1 Introduction 116
7.1.1 Intrusion and Detection 116
7.1.2 Some Basic Definitions 116
7.1.3 Intrusion Detection and Prevention System 117
7.1.4 Need for IDPS: More Than Ever 118
7.1.5 Introduction to Alarms 118
7.1.6 Components of an IDPS 119
7.2 Configuring IDPS 120
7.2.1 Network Architecture of IDPS 120
7.2.2 A Glance at Common Types 121
7.2.2.1 Network-Based IDS 123
7.2.2.2 Host-Based IDS 124
7.2.3 Intrusion Detection Techniques 125
7.2.3.1 Conventional Techniques 125
7.2.3.2 Machine Learning-Based and Hybrid Techniques 128
7.2.4 Three Considerations 131
7.2.4.1 Location of Sensors 131
7.2.4.2 Security Capabilities 131
7.2.4.3 Management Capabilities 133
7.2.5 Administratorsâ Functions 134
7.2.5.1 Deployment 134
7.2.5.2 Testing 134
7.2.5.3 Security Consideration of IDPS 135
7.2.5.4 Regular Backups and Monitoring 135
7.2.6 Types of Events Detected 135
7.2.7 Role of State in Network Security 136
7.3 Literature Review 137
7.4 Conclusion 138
References 139
8 Hardware Devices Integration With IoT 143
Sushant Kumar and Saurabh Mukherjee
8.1 Introduction 143
8.2 Literature Review 144
8.3 Component Description 146
8.3.1 Arduino Board UNO 146
8.3.2 Raspberry Pi 147
8.4 Case Studies 148
8.4.1 Ultrasonic Sensor 148
8.4.2 Temperature and Humidity Sensor 150
8.4.3 Weather Monitoring System Using Raspberry Pi 151
8.5 Drawbacks of Arduino and Raspberry Pi 153
8.6 Challenges in IoT 154
8.6.1 Design Challenges 154
8.6.2 Security Challenges 155
8.6.3 Development Challenges 155
8.7 Conclusion 155
8.8 Annexures 156
References 157
Additional Resources 158
9 Depth Analysis On DoS & DDoS Attacks 159
Gaurav Nayak, Anjana Mishra, Uditman Samal and Brojo Kishore Mishra
9.1 Introduction 160
9.1.1 Objective and Motivation 161
9.1.2 Symptoms and Manifestations 163
9.2 Literature Survey 163
9.3 Timeline of DoS and DDoS Attacks 164
9.4 Evolution of Denial of Service (DoS) & Distributed Denial of Service (DDoS) 165
9.5 DDoS Attacks: A Taxonomic Classification 166
9.5.1 Classification Based on Degree of Automation 166
9.5.2 Classification Based on Exploited Vulnerability 167
9.5.3 Classification Based on Rate Dynamics of Attacks 168
9.5.4 Classification Based on Impact 168
9.6 Transmission Control Protocol 169
9.6.1 TCP Three-Way Handshake 169
9.7 User Datagram Protocol 170
9.7.1 UDP Header 170
9.8 Types of DDoS Attacks 170
9.8.1 TCP SYN Flooding Attack 171
9.8.2 UDP Flooding Attack 172
9.8.3 Smurf Attack 172
9.8.4 Ping of Death Attack 173
9.8.5 HTTP Flooding Attack 174
9.9 Impact of DoS/DDoS on Various Areas 175
9.9.1 DoS/DDoS Attacks on VoIP Networks Using SIP 175
9.9.2 DoS/DDoS Attacks on VANET 175
9.9.3 DoS/DDoS Attacks on Smart Grid System 176
9.9.4 DoS/DDoS Attacks in IoT-Based Devices 176
9.10 Countermeasures to DDoS Attack 177
9.10.1 Prevent Being Agent/Secondary Target 177
9.10.2 Detect and Neutralize Attacker 178
9.10.3 Potential Threats Detection/Prevention 178
9.10.4 DDoS Attacks and How to Avoid Them 178
9.10.5 Deflect Attack 178
9.10.6 Post-Attack Forensics 179
9.11 Conclusion 179
9.12 Future Scope 180
References 180
10 SQL Injection Attack on Database System 183
Mohit Kumar
10.1 Introduction 183
10.1.1 Types of Vulnerabilities 184
10.1.2 Types of SQL Injection Attack 185
10.1.3 Impact of SQL Injection Attack 186
10.2 Objective and Motivation 186
10.3 Process of SQL Injection Attack 188
10.4 Related Work 188
10.5 Literature Review 189
10.6 Implementation of the SQL Injection Attack 192
10.6.1 Access the Database Using the 1=1 SQL Injection Statement 192
10.6.2 Access the Database Using the ââ=ââââ SQL Injection Statement 193
10.6.3 Access and Upgrade the Database by Using Batch SQL Injection Statement 194
10.7 Detection of SQL Injection Attack 196
10.8 Prevention/Mitigation from SQL Injection Attack 196
10.9 Conclusion 197
References 197
11 Machine Learning Techniques for Face Authentication System for Security Purposes 199
Vibhuti Jain, Madhavendra Singh and Jagannath Jayanti
11.1 Introduction 200
11.2 Face Recognition System (FRS) in Security 201
11.3 Theory 202
11.3.1 Neural Networks 202
11.3.2 Convolutional Neural Network (CNN) 204
11.3.3 K-Nearest Neighbors (KNN) 207
11.3.4 Support Vector Machine (SVM) 208
11.3.5 Logistic Regression (LR) 209
11.3.6 Naive Bayes (NB) 210
11.3.7 Decision Tree (DT) 211
11.4 Experimental Methodology 212
11.4.1 Dataset 212
11.4.2 Convolutional Neural Network (CNN) 212
11.4.3 Other Machine Learning Techniques 215
11.5 Results 218
11.6 Conclusion 220
References 220
12 Estimation of Computation Time for Software-Defined Networking-Based Data Traffic Offloading System in Heterogeneous Network 223
Shashila S. Abayagunawardhana, Malka N. Halgamuge and Charitha Subhashi Jayasekara
12.1 Introduction 224
12.1.1 Motivation 225
12.1.2 Objective 228
12.1.3 The Main Contributions of This Chapter 228
12.2 Analysis of SDN-TOS Mechanism 229
12.2.1 Key Components of SDN-TOS 229
12.2.2 LTE/Wi-Fi in a Heterogeneous Network (HetNet) 229
12.2.3 Centralized SDN Controller 229
12.2.4 Key Design Considerations of SDN-TOS 230
12.2.4.1 The System Architecture 230
12.2.4.2 Mininet Wi-Fi Emulated Networks 230
12.2.4.3 Software-Defined Networking Controller 231
12.3 Materials and Methods 232
12.3.1 Estimating Time Consumption for Mininet Wi-Fi Emulator 232
12.3.1.1 Total Time Consumption for Offloading the Data Traffic by Service Provider 233
12.3.1.2 Total Time Consumption of Mininet Wi-Fi Emulator (Time Consumption for Both LTE and Wi-Fi Network) 236
12.3.2 Estimating Time Consumption for SDN Controller 237
12.3.2.1 Total Response Time for Sub-Controller 237
12.3.2.2 Total Response Time for The Total Process of Centralized SDN Controller 238
12.3.3 Estimating Total Time Consumption for SDN-Based Traffic Offloading System (sdn-tos) 239
12.4 Simulation Results 240
12.4.1 Effect of Computational Data Traffic θI on Total Response Time (TA)/Service Provider A and CSP Approach 242
12.4.2 Effect of Computational Data Traffic θI on Total Response Time (TA) for Different Service Providers/Service Provider A and Service Provider B 243
12.5 Discussion 244
12.6 Conclusion 246
References 247
About the Editors 253
Index 255
