Wireless Communication Security

ISBN-13: 9781119777144 / Angielski / Twarda / 2023 / 350 str.

M. Niranjanamurthy

Spis treści

Preface xiii1 M2M in 5G Cellular Networks: Challenges, Proposed Solutions, and Future Directions 1Kiran Ahuja and Indu Bala1.1 Introduction 21.2 Literature Survey 51.3 Survey Challenges and Proposed Solutions of M2M 71.3.1 PARCH Overload Problem 81.3.2 Inefficient Radio Resource Utilization and Allocation 101.3.3 M2M Random Access Challenges 121.3.4 Clustering Techniques 131.3.5 QoS Provisioning for M2M Communications 151.3.6 Less Cost and Low Power Device Requirements 161.3.7 Security and Privacy 171.4 Conclusion 18References 192 MAC Layer Protocol for Wireless Security 23Sushmita Kumari and Manisha Bharti2.1 Introduction 232.2 MAC Layer 242.2.1 Centralized Control 242.2.2 Deterministic Access 242.2.3 Non-Deterministic Access 242.3 Functions of the MAC Layer 252.4 MAC Layer Protocol 252.4.1 Random Access Protocol 262.4.2 Controlled Access Protocols 292.4.3 Channelization 312.5 MAC Address 312.6 Conclusion and Future Scope 33References 333 Enhanced Image Security Through Hybrid Approach: Protect Your Copyright Over Digital Images 35Shaifali M. Arora and Poonam Kadian3.1 Introduction 363.2 Literature Review 383.3 Design Issues 403.3.1 Robustness Against Various Attack Conditions 403.3.2 Distortion and Visual Quality 413.3.3 Working Domain 423.3.4 Human Visual System (HVS) 433.3.5 The Trade-Off between Robustness and Imperceptibility 433.3.6 Computational Cost 433.4 A Secure Grayscale Image Watermarking Based on DWT-SVD 433.5 Experimental Results 453.6 Conclusion 52References 524 Quantum Computing 59Manisha Bharti and Tanvika Garg4.1 Introduction 594.2 A Brief History of Quantum Computing 604.3 Postulate of Quantum Mechanics 614.4 Polarization and Entanglement 614.5 Applications and Advancements 634.5.1 Cryptography, Teleportation and Communication Networks 634.5.2 Quantum Computing and Memories 634.5.3 Satellite Communication Based on Quantum Computing 644.5.4 Machine Learning & Artificial Intelligence 654.6 Optical Quantum Computing 654.7 Experimental Realisation of Quantum Computer 664.7.1 Hetero-Polymers 664.7.2 Ion Traps 674.7.3 Quantum Electrodynamics Cavity 674.7.4 Quantum Dots 674.8 Challenges of Quantum Computing 674.9 Conclusion and Future Scope 68References 685 Feature Engineering for Flow-Based IDS 69Rahul B. Adhao and Vinod K. Pachghare5.1 Introduction 705.1.1 Intrusion Detection System 715.1.2 IDS Classification 715.2 IP Flows 725.2.1 The Architecture of Flow-Based IDS 735.2.2 Wireless IDS Designed Using Flow-Based Approach 735.2.3 Comparison of Flow- and Packet-Based IDS 745.3 Feature Engineering 755.3.1 Curse of Dimensionality 765.3.2 Feature Selection 785.3.3 Feature Categorization 785.4 Classification of Feature Selection Technique 785.4.1 The Wrapper, Filter, and Embedded Feature Selection 785.4.2 Correlation, Consistency, and PCA-Based Feature Selection 805.4.3 Similarity, Information Theoretical, Sparse Learning, and Statistical-Based Feature Selection 805.4.4 Univariate and Multivariate Feature Selection 815.5 Tools and Library for Feature Selection 825.6 Literature Review on Feature Selection in Flow-Based IDS 825.7 Challenges and Future Scope 865.8 Conclusions 87Acknowledgement 87References 886 Environmental Aware Thermal (EAT) Routing Protocol for Wireless Sensor Networks 91B. Banuselvasaraswathy and Vimalathithan Rathinasabapathy6.1 Introduction 926.1.1 Single Path Routing Protocol 936.1.2 Multipath Routing Protocol 946.1.3 Environmental Influence on WSN 966.2 Motivation Behind the Work 976.3 Novelty of This Work 986.4 Related Works 996.5 Proposed Environmental Aware Thermal (EAT) Routing Protocol 1026.5.1 Sensor Node Environmental Modeling and Analysis 1046.5.2 Single Node Environmental Influence Modeling 1056.5.3 Multiple Node Modeling 1066.5.4 Sensor Node Surrounding Temperature Field 1066.5.5 Sensor Node Remaining Energy Calculation 1076.5.6 Delay Modeling 1076.6 Simulation Parameters 1086.7 Results and Discussion 1096.7.1 Temperature Influence on Network 1096.7.2 Power Consumption 1096.7.3 Lifetime Analysis 1106.7.4 Delay Analysis 1116.8 Conclusion 112References 1127 A Comprehensive Study of Intrusion Detection and Prevention Systems 115Bhoopesh Singh Bhati, Dikshita, Nitesh Singh Bhati and Garvit Chugh7.1 Introduction 1167.1.1 Intrusion and Detection 1167.1.2 Some Basic Definitions 1167.1.3 Intrusion Detection and Prevention System 1177.1.4 Need for IDPS: More Than Ever 1187.1.5 Introduction to Alarms 1187.1.6 Components of an IDPS 1197.2 Configuring IDPS 1207.2.1 Network Architecture of IDPS 1207.2.2 A Glance at Common Types 1217.2.2.1 Network-Based IDS 1237.2.2.2 Host-Based IDS 1247.2.3 Intrusion Detection Techniques 1257.2.3.1 Conventional Techniques 1257.2.3.2 Machine Learning-Based and Hybrid Techniques 1287.2.4 Three Considerations 1317.2.4.1 Location of Sensors 1317.2.4.2 Security Capabilities 1317.2.4.3 Management Capabilities 1337.2.5 Administrators' Functions 1347.2.5.1 Deployment 1347.2.5.2 Testing 1347.2.5.3 Security Consideration of IDPS 1357.2.5.4 Regular Backups and Monitoring 1357.2.6 Types of Events Detected 1357.2.7 Role of State in Network Security 1367.3 Literature Review 1377.4 Conclusion 138References 1398 Hardware Devices Integration With IoT 143Sushant Kumar and Saurabh Mukherjee8.1 Introduction 1438.2 Literature Review 1448.3 Component Description 1468.3.1 Arduino Board UNO 1468.3.2 Raspberry Pi 1478.4 Case Studies 1488.4.1 Ultrasonic Sensor 1488.4.2 Temperature and Humidity Sensor 1508.4.3 Weather Monitoring System Using Raspberry Pi 1518.5 Drawbacks of Arduino and Raspberry Pi 1538.6 Challenges in IoT 1548.6.1 Design Challenges 1548.6.2 Security Challenges 1558.6.3 Development Challenges 1558.7 Conclusion 1558.8 Annexures 156References 157Additional Resources 1589 Depth Analysis On DoS & DDoS Attacks 159Gaurav Nayak, Anjana Mishra, Uditman Samal and Brojo Kishore Mishra9.1 Introduction 1609.1.1 Objective and Motivation 1619.1.2 Symptoms and Manifestations 1639.2 Literature Survey 1639.3 Timeline of DoS and DDoS Attacks 1649.4 Evolution of Denial of Service (DoS) & Distributed Denial of Service (DDoS) 1659.5 DDoS Attacks: A Taxonomic Classification 1669.5.1 Classification Based on Degree of Automation 1669.5.2 Classification Based on Exploited Vulnerability 1679.5.3 Classification Based on Rate Dynamics of Attacks 1689.5.4 Classification Based on Impact 1689.6 Transmission Control Protocol 1699.6.1 TCP Three-Way Handshake 1699.7 User Datagram Protocol 1709.7.1 UDP Header 1709.8 Types of DDoS Attacks 1709.8.1 TCP SYN Flooding Attack 1719.8.2 UDP Flooding Attack 1729.8.3 Smurf Attack 1729.8.4 Ping of Death Attack 1739.8.5 HTTP Flooding Attack 1749.9 Impact of DoS/DDoS on Various Areas 1759.9.1 DoS/DDoS Attacks on VoIP Networks Using SIP 1759.9.2 DoS/DDoS Attacks on VANET 1759.9.3 DoS/DDoS Attacks on Smart Grid System 1769.9.4 DoS/DDoS Attacks in IoT-Based Devices 1769.10 Countermeasures to DDoS Attack 1779.10.1 Prevent Being Agent/Secondary Target 1779.10.2 Detect and Neutralize Attacker 1789.10.3 Potential Threats Detection/Prevention 1789.10.4 DDoS Attacks and How to Avoid Them 1789.10.5 Deflect Attack 1789.10.6 Post-Attack Forensics 1799.11 Conclusion 1799.12 Future Scope 180References 18010 SQL Injection Attack on Database System 183Mohit Kumar10.1 Introduction 18310.1.1 Types of Vulnerabilities 18410.1.2 Types of SQL Injection Attack 18510.1.3 Impact of SQL Injection Attack 18610.2 Objective and Motivation 18610.3 Process of SQL Injection Attack 18810.4 Related Work 18810.5 Literature Review 18910.6 Implementation of the SQL Injection Attack 19210.6.1 Access the Database Using the 1=1 SQL Injection Statement 19210.6.2 Access the Database Using the ""='''' SQL Injection Statement 19310.6.3 Access and Upgrade the Database by Using Batch SQL Injection Statement 19410.7 Detection of SQL Injection Attack 19610.8 Prevention/Mitigation from SQL Injection Attack 19610.9 Conclusion 197References 19711 Machine Learning Techniques for Face Authentication System for Security Purposes 199Vibhuti Jain, Madhavendra Singh and Jagannath Jayanti11.1 Introduction 20011.2 Face Recognition System (FRS) in Security 20111.3 Theory 20211.3.1 Neural Networks 20211.3.2 Convolutional Neural Network (CNN) 20411.3.3 K-Nearest Neighbors (KNN) 20711.3.4 Support Vector Machine (SVM) 20811.3.5 Logistic Regression (LR) 20911.3.6 Naive Bayes (NB) 21011.3.7 Decision Tree (DT) 21111.4 Experimental Methodology 21211.4.1 Dataset 21211.4.2 Convolutional Neural Network (CNN) 21211.4.3 Other Machine Learning Techniques 21511.5 Results 21811.6 Conclusion 220References 22012 Estimation of Computation Time for Software-Defined Networking-Based Data Traffic Offloading System in Heterogeneous Network 223Shashila S. Abayagunawardhana, Malka N. Halgamuge and Charitha Subhashi Jayasekara12.1 Introduction 22412.1.1 Motivation 22512.1.2 Objective 22812.1.3 The Main Contributions of This Chapter 22812.2 Analysis of SDN-TOS Mechanism 22912.2.1 Key Components of SDN-TOS 22912.2.2 LTE/Wi-Fi in a Heterogeneous Network (HetNet) 22912.2.3 Centralized SDN Controller 22912.2.4 Key Design Considerations of SDN-TOS 23012.2.4.1 The System Architecture 23012.2.4.2 Mininet Wi-Fi Emulated Networks 23012.2.4.3 Software-Defined Networking Controller 23112.3 Materials and Methods 23212.3.1 Estimating Time Consumption for Mininet Wi-Fi Emulator 23212.3.1.1 Total Time Consumption for Offloading the Data Traffic by Service Provider 23312.3.1.2 Total Time Consumption of Mininet Wi-Fi Emulator (Time Consumption for Both LTE and Wi-Fi Network) 23612.3.2 Estimating Time Consumption for SDN Controller 23712.3.2.1 Total Response Time for Sub-Controller 23712.3.2.2 Total Response Time for The Total Process of Centralized SDN Controller 23812.3.3 Estimating Total Time Consumption for SDN-Based Traffic Offloading System (sdn-tos) 23912.4 Simulation Results 24012.4.1 Effect of Computational Data Traffic thetaI on Total Response Time (TA)/Service Provider A and CSP Approach 24212.4.2 Effect of Computational Data Traffic thetaI on Total Response Time (TA) for Different Service Providers/Service Provider A and Service Provider B 24312.5 Discussion 24412.6 Conclusion 246References 247About the Editors 253Index 255

O autorze

Manju Khari, PhD, is an assistant professor in AIACTR, affiliated with GGSIP University, Delhi, India. She is also the professor-in-charge of the IT Services of the Institute and has experience of more than twelve years in network planning and management. She holds a PhD in computer science and engineering from the National Institute of Technology, Patna.Manisha Bharti, PhD, is an assistant professor at the National Institute of Technology (NIT) Delhi, India. She received her PhD from IKG Punjab Technical University, Jalandhar and has over 12 years of teaching and research experience.M. Niranjanamurthy, PhD, is an assistant professor in the Department of Computer Applications, M S Ramaiah Institute of Technology, Bangalore, Karnataka. He earned his PhD in computer science at JJTU. He has over 10 years of teaching experience and two years of industry experience as a software engineer. He has two patents to his credit and has won numerous awards. He has published four books, and he is currently working on numerous books for Scrivener Publishing. He has also published over 50 papers in scholarly journals.