The main objective of this book is to introduce cyber security using modern technologies such as Artificial Intelligence, Quantum Cryptography, and Blockchain. This book provides in-depth coverage of important concepts related to cyber security. Beginning with an introduction to Quantum Computing, Post-Quantum Digital Signatures, and Artificial Intelligence for cyber security of modern networks and covering various cyber-attacks and the defense measures, strategies, and techniques that need to be followed to combat them, this book goes on to explore several crucial topics, such as security of advanced metering infrastructure in smart grids, key management protocols, network forensics, intrusion detection using machine learning, cloud computing security risk assessment models and frameworks, cyber-physical energy systems security, a biometric random key generator using deep neural network and encrypted network traffic classification. In addition, this book provides new techniques to handle modern threats with more intelligence. It also includes some modern techniques for cyber security, such as blockchain for modern security, quantum cryptography, and forensic tools. Also, it provides a comprehensive survey of cutting-edge research on the cyber security of modern networks, giving the reader a general overview of the field. It also provides interdisciplinary solutions to protect modern networks from any type of attack or manipulation. The new protocols discussed in this book thoroughly examine the constraints of networks, including computation, communication, and storage cost constraints, and verifies the protocols both theoretically and experimentally. Written in a clear and comprehensive manner, this book would prove extremely helpful to readers. This unique and comprehensive solution for the cyber security of modern networks will greatly benefit researchers, graduate students, and engineers in the fields of cryptography and network security.
Author(s): Om Pal; Vinod Kumar; Rijwan Khan; Bashir Alam; Mansaf Alam
Year: 2023
Language: English
Pages: 702
Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgments
Editors
List of Contributors
Chapter 1 Quantum Computing: A Global Scenario
1.1 Introduction
1.2 Quantum Computing Terminology
1.2.1 Qubit
1.2.2 Superposition
1.2.3 Parallelism
1.2.4 Entanglement
1.3 Quantum Gates
1.3.1 Controlled NOT (CNOT) Gate
1.3.2 Hadamard Gate
1.3.3 Pauli-X Gate
1.3.4 Toffoli Gate
1.4 Need of Quantum Computing
1.5 Quantum Computing Approaches and Challenges
1.6 Quantum Computing Research Status
1.6.1 Quantum Computing Research Status in India
1.7 Conclusion and Future Directions
References
Chapter 2 Post-Quantum Digital Signatures
2.1 Introduction
2.1.1 Section-Wise Plan
2.1.2 Background Expected of the Reader
2.2 Preliminaries and Notations
2.2.1 Digital Signatures and Their Security
2.2.2 Secure Signatures in ROM
2.2.3 Modelling Quantum Adversary
2.3 NIST PQC Standardization Competition
2.3.1 Round Three Candidates
2.4 Lattice-Based Signatures
2.4.1 Hard Problems
2.4.2 NTRUSign
2.4.3 GPV Framework
2.4.3.1 Falcon Signature Scheme
2.4.4 Fiat-Shamir with Aborts
2.4.4.1 CRYSTALS-DILITHIUM Signature Scheme
2.5 MQ-Based Signatures
2.5.1 MQ-Based Hard Problems
2.5.2 Oil-Vinegar Signatures
2.5.2.1 Unbalanced Oil-Vinegar Signature
2.5.2.2 Rainbow Signature Scheme
2.5.2.3 LUOV Signature Scheme
2.5.3 HFE Signatures
2.5.3.1 GeMSS
2.6 Signatures Based on Symmetric Key Techniques
2.6.1 Picnic
2.6.1.1 ZKBoo
2.6.1.2 ZKB++ and Picnic
2.6.2 SPHINCS+
2.7 Signatures Based on Supersingular Isogenies
2.7.1 Preliminaries on Elliptic Curves
2.7.2 Yoo et al. Scheme
2.7.3 A Discussion on Other Isogeny-Based Signatures
2.8 Some Interesting Use Cases
2.8.1 Certification Authority and Authentication in TLS
2.8.2 Secure and Verified Boot
2.8.3 Miscellaneous Applications
2.8.4 Challenges in Standardization, Migration, and Ubiquitous Usage of Such Schemes
2.9 Conclusions
Acknowledgements
Notes
References
Chapter 3 Analysis of Quantum Computing with Food Processing Use Case
3.1 Introduction
3.1.1 Need for Computational Analysis of Quantum Computing
3.1.2 Issues and Challenges in the Area of Quantum Computing
3.1.3 Applications of Quantum Computing
3.1.4 Paper Organization
3.2 Related Work
3.3 Role of Quantum Computing for HPC
3.3.1 Programming Model of Quantum Computing
3.3.2 Architecture of Quantum Computing
3.3.3 Methodology and Concepts for Quantum Computing
3.4 Quantum Computing Use Case for Food Processing
3.4.1 Proposed System Architecture
3.4.2 Applicability of Expected Outcomes
3.5 Summary
References
Chapter 4 Security of Modern Networks and its Challenges
4.1 Introduction to Modern Networks
4.2 Security of Modern Networks
4.2.1 How Do We Deal with Network Security?
4.3 Types of Security Attacks
4.4 Modern Network Security Methods
4.5 Network Security Tools
4.6 Network Security Challenges
4.7 Conclusion
References
Chapter 5 Security and Performance Analysis of Advanced Metering Infrastructure in Smart Grid and Use of Blockchain in Security Perspective
5.1 Introduction
5.2 Background
5.3 Key Management Protocols
5.4 Blockchain in AMI of SG for Security
5.5 Comparative Analysis
5.6 Future Research Directions
5.7 Conclusion
References
Chapter 6 Computation and Storage Efficient Key Distribution Protocol for Secure Multicast Communication in Centralized Environments
6.1 Introduction
6.2 Related Work
6.3 Proposed Architecture for Key Distribution in Centralized Environments
6.4 Proposed CSKD Protocol
6.4.1 Initialization Phase
6.4.2 Initial Member Join
6.4.3 Key Update
6.4.3.1 Adding Member
6.4.3.2 Leaving Member
6.4.4 Key Recovery
6.5 Security Analysis
6.5.1 Forward Secrecy
6.5.2 Backward Secrecy
6.5.3 Passive Attack
6.5.4 Collision Attack
6.5.5 Reply Attack
6.6 Performance Analysis
6.7 Experimental Results
6.8 Conclusion
References
Chapter 7 Effective Key Agreement Protocol for Large and Dynamic Groups Using Elliptic Curve Cryptography
7.1 Introduction
7.2 Related Work
7.3 Proposed Distributed Key Management Protocol
7.3.1 Initialization Phase
7.3.2 Batch Rekeying
7.3.3 Procedure for Finding IP
7.3.4 Procedure for Pruning
7.4 Performance Analysis
7.5 Implementation Results
7.6 Conclusion
References
Chapter 8 Cyber Security Using Artificial Intelligence
8.1 Introduction
8.2 Cyber Security
8.3 Cyber Threats
8.4 AI-Based Systems Support Cyber Security
8.5 Benefits of AI in Cyber Security
8.6 AI-Based Cyber Security Tools
8.7 Growth of AI in Cyber Security
8.8 Challenges and Limitations
8.9 Conclusion
References
Chapter 9 Cloud Computing: An Overview of Security Risk Assessment Models and Frameworks
9.1 Introduction
9.2 Existing Security Risk Assessment Models & Frameworks
9.2.1 Cloud Risk Assessment Models
9.2.1.1 Cloud Adoption Risk Assessment Model
9.2.1.2 Consultative, Objective, and Bi-Functional Risk Analysis
9.2.2 Cloud Risk Assessment Frameworks
9.2.2.1 Cloud Security Risk Management Framework
9.2.3 Information Security Risk Management Framework
9.2.4 Security Risk Assessment Framework
9.3 Performance Analysis of the Existing Models and Frameworks
9.3.1 Does the Framework Effectively Address Both Phases of Risk Management (Risk Assessment and Risk Treatment)?
9.3.2 Does the Framework Enable the CSP and the Customer to Efficiently Assess and Mitigate Cloud Security Risks?
9.4 Conclusion and Future Directions
Acknowledgment
References
Chapter 10 Generating Cyber Threat Intelligence to Discover Potential Security Threats Using Classification and Topic Modeling
10.1 Introduction
10.1.1 Background and Motivation
10.1.2 Problem Statement and Goal
10.2 Methodology
10.2.1 Data Collection
10.2.2 Preprocessing and Dataset Construction
10.2.2.1 Binary Dataset Construction
10.2.2.2 Multi-Class Dataset Construction
10.2.3 Feature Engineering
10.2.4 Supervised Method: Classification
10.2.5 Unsupervised Method: Topic Modeling
10.3 Experimental Setup
10.4 Experimental Results
10.5 Discussion on Results
10.6 Challenges and Future Scopes
10.7 Conclusion
References
Chapter 11 Cyber-Physical Energy Systems Security: Attacks, Vulnerabilities and Risk Management
11.1 Introduction
11.1.1 CPES Components
11.1.1.1 Sensing Components
11.1.1.2 Controlling Components
11.1.2 CPES Layers
11.1.3 CPES Security Concerns
11.1.4 Contribution of this Chapter
11.2 Related Work
11.3 CPES Threats and Vulnerabilities
11.3.1 CPES Security Threats
11.3.1.1 Cyber Threats
11.3.1.2 Physical Threats
11.3.2 CPES Vulnerabilities
11.3.2.1 Cyber Vulnerabilities
11.3.2.2 Physical Vulnerabilities
11.4 Cyber-Attacks and Cyber Security in CPES
11.4.1 Passive Attacks
11.4.2 Active Attacks
11.5 Cyber-Attack Analysis
11.5.1 Some Recent Cyber-Attacks in CPES
11.5.2 CPES-Specific Attacks: Case Study
11.5.2.1 Case Study 1: Cross-Layer Firmware Attacks
11.5.2.2 Case Study 2: Load-Changing Attacks
11.5.2.3 Case Study 3: Time-Delay Attacks
11.5.2.4 Case Study 4: Propagating Attacks on Integrated Transmission and Distribution CPES
11.6 CPES Risk Evaluation
11.6.1 Risk Identification and Management
11.6.2 Risk Assessment
11.6.3 Risk Impact
11.6.4 Risk Mitigation
11.6.5 CPES Forensics
11.7 Ground for Future Work
11.8 Conclusion
References
Chapter 12 Intrusion Detection Using Machine Learning
12.1 Introduction
12.1.1 IDS Classification
12.1.2 Why IDS?
12.2 Related Work
12.3 Experiment
12.3.1 Data Preprocessing
12.3.1.1 Transformation Operation
12.3.1.2 Normalization Operation
12.3.2 Proposed Feature Selection Methods
12.3.2.1 The Technique for the Combination of Various Algorithms for Selecting Features
12.3.2.2 The Combining Technique of Various Characteristic Choice Set of Rules Consistent with Protocol Type
12.3.3 Evaluation
12.4 Result
12.5 Conclusion
References
Chapter 13 Network Forensics
13.1 Introduction
13.2 Methodology for Network Forensics
13.2.1 Identification
13.2.2 Preservation
13.2.3 Collection
13.2.4 Examination
13.2.5 Analysis
13.2.6 Presentation
13.2.7 Incident Response
13.3 Sources of Evidence
13.3.1 Spout the Wire and the Air (TAPs)
13.3.2 CAM Table on a Network Switch
13.3.3 Routing Tables Function for Routers
13.3.4 Domain Controller/Authentication Servers/System Records
13.3.5 IDS/IPS Records
13.3.6 Proxy Server Records
13.4 Tools in Digital Forensics
13.4.1 Tcpdump (Command Line)
13.4.2 Wireshark (Graphical User Interface)
13.4.3 Network Miner
13.4.4 Splunk
13.4.5 Snort
13.4.6 The Sleuth Kit
13.4.7 Autopsy
13.4.8 ProDiscover Basic
13.4.9 SANS SIFT
13.4.10 Volatileness
13.5 Methodology in Digital Forensics
13.5.1 Preserving the Evidence
13.5.2 Web Scheme Reconstruction
13.5.3 File Signature Attestation
13.5.4 Network Device Inspection
13.5.5 Recovering Invisible Files
13.6 Conclusion
References
Chapter 14 A Deep Neural Network-Based Biometric Random Key Generator for Security Enhancement
14.1 Introduction
14.1.1 System Contributions
14.1.2 Chapter Organization
14.2 Review of Literature
14.3 Proposed System
14.3.1 Key Generation Unit
14.3.1.1 Neural Network
14.3.1.2 Multi-Task Cascaded Convolutional Neural Networks
14.3.1.3 Facenet
14.3.1.4 Round Off Operations
14.3.2 Design of LFSR
14.3.2.1 Pseudo-Random Number Generator
14.3.3 Encryption and Decryption Process
14.3.3.1 Encryption Unit
14.3.3.2 Decryption Unit
14.4 Implementation
14.4.1 MTCNN
14.4.1.1 P-Net
14.4.1.2 R-Net
14.4.1.3 O-Net
14.4.2 Facenet
14.4.3 LFSR
14.5 Results
14.5.1 Visual Presentation of the Encryption and Decryption Sequence
14.5.2 Tests on Subsequence Generated
14.5.2.1 Chi-Square Test
14.5.2.2 Run Up-Down Test
14.5.2.3 Performance Analysis of Encryption and Decryption
14.6 Conclusion
14.6.1 Limitations and Future Scope of the System
References
Chapter 15 Quantum Computing and its Real-World Applications
15.1 Introduction
15.2 Quantum Computing
15.2.1 Key Points of Quantum Theory
15.2.2 Qubit, Superposition, and Entanglement
15.2.3 Supremacy of Quantum Computing Over Classical Computer
15.2.4 Computer Computing vs Classical Computing
15.2.5 Rumors and Realities About Quantum Computing
15.3 Hand-Held Applications of Quantum Computing
15.3.1 Quantum Computing in Cyber Security
15.3.2 Quantum Computing in Cloud Computing
15.3.3 Quantum Computing in Evolutionary Computing
15.4 Discussion and Conclusion
References
Chapter 16 Encrypted Network Traffic Classification and Application Identification Employing Deep Learning
16.1 Introduction
16.2 Literature Review
16.3 Deep Learning and CNN
16.3.1 Deep Learning
16.3.2 Convolutional Neural Networks
16.4 Material and Methods
16.5 Dataset
16.6 Preprocessing
16.6.1 Labelling Dataset
16.6.2 Model Architecture
16.7 Experimental Results and Discussion
16.8 Conclusion
References
Index
