Cyber Security and Digital Forensics: Challenges and Future Trends

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
Acknowledgment
1 A Comprehensive Study of Security Issues and Research Challenges in Different Layers of ServiceOriented IoT Architecture
	1.1 Introduction and Related Work
	1.2 IoT: Evolution, Applications and Security Requirements
		1.2.1 IoT and Its Evolution
		1.2.2 Different Applications of IoT
		1.2.3 Different Things in IoT
		1.2.4 Security Requirements in IoT
	1.3 Service-Oriented IoT Architecture and IoT Protocol Stack
		1.3.1 Service-Oriented IoT Architecture
		1.3.2 IoT Protocol Stack
			1.3.2.1 Application Layer Protocols
			1.3.2.2 Transport Layer Protocols
			1.3.2.3 Network Layer Protocols
			1.3.2.4 Link Layer and Physical Layer Protocols
	1.4 Anatomy of Attacks on Service-Oriented IoT Architecture
		1.4.1 Attacks on Software Service
			1.4.1.1 Operating System–Level Attacks
			1.4.1.2 Application-Level Attacks
			1.4.1.3 Firmware-Level Attacks
		1.4.2 Attacks on Devices
		1.4.3 Attacks on Communication Protocols
			1.4.3.1 Attacks on Application Layer Protocols
			1.4.3.2 Attacks on Transport Layer Protocols
			1.4.3.3 Attacks on Network Layer Protocols
			1.4.3.4 Attacks on Link and Physical Layer Protocols
	1.5 Major Security Issues in Service-Oriented IoT Architecture
		1.5.1 Application – Interface Layer
		1.5.2 Service Layer
		1.5.3 Network Layer
		1.5.4 Sensing Layer
	1.6 Conclusion
	References
2 Quantum and Post-Quantum Cryptography
	2.1 Introduction
	2.2 Security of Modern Cryptographic Systems
		2.2.1 Classical and Quantum Factoring of A Large Number
		2.2.2 Classical and Quantum Search of An Item
	2.3 Quantum Key Distribution
		2.3.1 BB84 Protocol
			2.3.1.1 Proposed Key Verification Phase for BB84
		2.3.2 E91 Protocol
		2.3.3 Practical Challenges of Quantum Key Distribution
		2.3.4 Multi-Party Quantum Key Agreement Protocol
	2.4 Post-Quantum Digital Signature
		2.4.1 Signatures Based on Lattice Techniques
		2.4.2 Signatures Based on Multivariate Quadratic Techniques
		2.4.3 Hash-Based Signature Techniques
	2.5 Conclusion and Future Directions
	References
3 Artificial Neural Network Applications in Analysis of Forensic Science
	3.1 Introduction
	3.2 Digital Forensic Analysis Knowledge
	3.3 Answer Set Programming in Digital Investigations
	3.4 Data Science Processing with Artificial Intelligence Models
	3.5 Pattern Recognition Techniques
	3.6 ANN Applications
	3.7 Knowledge on Stages of Digital Forensic Analysis
	3.8 Deep Learning and Modelling
	3.9 Conclusion
	References
4 A Comprehensive Survey of Fully Homomorphic Encryption from Its Theory to Applications
	4.1 Introduction
	4.2 Homomorphic Encryption Techniques
		4.2.1 Partial Homomorphic Encryption Schemes
		4.2.2 Fully Homomorphic Encryption Schemes
	4.3 Homomorphic Encryption Libraries
	4.4 Computations on Encrypted Data
	4.5 Applications of Homomorphic Encryption
	4.6 Conclusion
	References
5 Understanding Robotics through Synthetic Psychology
	5.1 Introduction
	5.2 Physical Capabilities of Robots
		5.2.1 Artificial Intelligence and Neuro Linguistic Programming (NLP)
		5.2.2 Social Skill Development and Activity Engagement
		5.2.3 Autism Spectrum Disorders
		5.2.4 Age-Related Cognitive Decline and Dementia
		5.2.5 Improving Psychosocial Outcomes through Robotics
		5.2.6 Clients with Disabilities and Robotics
		5.2.7 Ethical Concerns and Robotics
	5.3 Traditional Psychology, Neuroscience and Future Robotics
	5.4 Synthetic Psychology and Robotics: A Vision of the Future
	5.5 Synthetic Psychology: The Foresight
	5.6 Synthetic Psychology and Mathematical Optimization
	5.7 Synthetic Psychology and Medical Diagnosis
		5.7.1 Virtual Assistance and Robotics
		5.7.2 Drug Discovery and Robotics
	5.8 Conclusion
	References
6 An Insight into Digital Forensics: History, Frameworks, Types and Tools
	6.1 Overview
	6.2 Digital Forensics
		6.2.1 Why Do We Need Forensics Process?
		6.2.2 Forensics Process Principles
	6.3 Digital Forensics History
		6.3.1 1985 to 1995
		6.3.2 1995 to 2005
		6.3.3 2005 to 2015
	6.4 Evolutionary Cycle of Digital Forensics
		6.4.1 Ad Hoc
		6.4.2 Structured Phase
		6.4.3 Enterprise Phase
	6.5 Stages of Digital Forensics Process
		6.5.1 Stage 1 1995 to 2003
		6.5.2 Stage II 2004 to 2007
		6.5.3 Stage III 2007 to 2014
	6.6 Types of Digital Forensics
		6.6.1 Cloud Forensics
		6.6.2 Mobile Forensics
		6.6.3 IoT Forensics
		6.6.4 Computer Forensics
		6.6.5 Network Forensics
		6.6.6 Database Forensics
	6.7 Evidence Collection and Analysis
	6.8 Digital Forensics Tools
		6.8.1 X-Ways Forensics
		6.8.2 SANS Investigative Forensics Toolkit – SIFT
		6.8.3 EnCase
		6.8.4 The Sleuth Kit/Autopsy
		6.8.5 Oxygen Forensic Suite
		6.8.6 Xplico
		6.8.7 Computer Online Forensic Evidence Extractor (COFEE)
		6.8.8 Cellebrite UFED
		6.8.9 OSForeniscs
		6.8.10 Computer-Aided Investigative Environment (CAINE)
	6.9 Summary
	References
7 Digital Forensics as a Service: Analysis for Forensic Knowledge
	7.1 Introduction
	7.2 Objective
	7.3 Types of Digital Forensics
		7.3.1 Network Forensics
		7.3.2 Computer Forensics
		7.3.3 Data Forensics
		7.3.4 Mobile Forensics
		7.3.5 Big Data Forensics
		7.3.6 IoT Forensics
		7.3.7 Cloud Forensics
	7.4 Conclusion
	References
8 4S Framework: A Practical CPS Design Security Assessment & Benchmarking Framework
	8.1 Introduction
	8.2 Literature Review
	8.3 Medical Cyber Physical System (MCPS)
		8.3.1 Difference between CPS and MCPS
		8.3.2 MCPS Concerns, Potential Threats, Security
	8.4 CPSSEC vs. Cyber Security
	8.5 Proposed Framework
		8.5.1 4S Definitions
		8.5.2 4S Framework-Based CPSSEC Assessment Process:
		8.5.3 4S Framework-Based CPSSEC Assessment Score Breakdown & Formula
	8.6 Assessment of Hypothetical MCPS Using 4S Framework
		8.6.1 System Description
		8.6.2 Use Case Diagram for the Above CPS
		8.6.3 Iteration 1 of 4S Assessment
		8.6.4 Iteration 2 of 4S Assessment
	8.7 Conclusion
	8.8 Future Scope
	References
9 Ensuring Secure Data Sharing in IoT Domains Using Blockchain
	9.1 IoT and Blockchain
		9.1.1 Public
			9.1.1.1 Proof of Work (PoW)
			9.1.1.2 Proof of Stake (PoS)
			9.1.1.3 Delegated Proof of Stake (DPoS)
		9.1.2 Private
		9.1.3 Consortium or Federated
	9.2 IoT Application Domains and Challenges in Data Sharing
	9.3 Why Blockchain?
	9.4 IoT Data Sharing Security Mechanism On Blockchain
		9.4.1 Double-Chain Mode Based On Blockchain Technology
		9.4.2 Blockchain Structure Based On Time Stamp
	9.5 Conclusion
	References
10 A Review of Face Analysis Techniques for Conventional and Forensic Applications
	10.1 Introduction
	10.2 Face Recognition
		10.2.1 Literature Review on Face Recognition
		10.2.2 Challenges in Face Recognition
		10.2.3 Applications of Face Recognition
	10.3 Forensic Face Recognition
		10.3.1 Literature Review on Face Recognition for Forensics
		10.3.2 Challenges of Face Recognition in Forensics
		10.3.3 Possible Datasets Used for Forensic Face Recognition
		10.3.4 Fundamental Factors for Improving Forensics Science
		10.3.5 Future Perspectives
	10.4 Conclusion
	References
11 Roadmap of Digital Forensics Investigation Process with Discovery of Tools
	11.1 Introduction
	11.2 Phases of Digital Forensics Process
		11.2.1 Phase I Identification
		11.2.2 Phase II Acquisition and Collection
		11.2.3 Phase III Analysis and Examination
		11.2.4 Phase IV Reporting
	11.3 Analysis of Challenges and Need of Digital Forensics
		11.3.1 Digital Forensics Process has following Challenges
		11.3.2 Needs of Digital Forensics Investigation
		11.3.3 Other Common Attacks Used to Commit the Crime
	11.4 Appropriateness of Forensics Tool
		11.4.1 Level of Skill
		11.4.2 Outputs
		11.4.3 Region of Emphasis
		11.4.4 Support for Additional Hardware
	11.5 Phase-Wise Digital Forensics Techniques
		11.5.1 Identification
		11.5.2 Acquisition
		11.5.3 Analysis
			11.5.3.1 Data Carving
			11.5.3.2 Different Curving Techniques
			11.5.3.3 Volatile Data Forensic Toolkit Used to Collect and Analyze the Data from Device
		11.5.4 Report Writing
	11.6 Pros and Cons of Digital Forensics Investigation Process
		11.6.1 Advantages of Digital Forensics
		11.6.2 Disadvantages of Digital Forensics
	11.7 Conclusion
	References
12 Utilizing Machine Learning and Deep Learning in Cybesecurity: An Innovative Approach
	12.1 Introduction
		12.1.1 Protections of Cybersecurity
		12.1.2 Machine Learning
		12.1.3 Deep Learning
		12.1.4 Machine Learning and Deep Learning: Similarities and Differences
	12.2 Proposed Method
		12.2.1 The Dataset Overview
		12.2.2 Data Analysis and Model for Classification
	12.3 Experimental Studies and Outcomes Analysis
		12.3.1 Metrics on Performance Assessment
		12.3.2 Result and Outcomes
			12.3.2.1 Issue 1: Classify the Various Categories of Feedback Related to the Malevolent Code Provided
			12.3.2.2 Issue 2: Recognition of the Various Categories of Feedback Related to the Malware Presented
			12.3.2.3 Issue 3: According to the Malicious Code, Distinguishing Various Forms of Malware
			12.3.2.4 Issue 4: Detection of Various Malware Styles Based on Different Responses
		12.3.3 Discussion
	12.4 Conclusions and Future Scope
	References
13 Applications of Machine Learning Techniques in the Realm of Cybersecurity
	13.1 Introduction
	13.2 A Brief Literature Review
	13.3 Machine Learning and Cybersecurity: Various Issues
		13.3.1 Effectiveness of ML Technology in Cybersecurity Systems
		13.3.2 Machine Learning Problems and Challenges in Cybersecurity
			13.3.2.1 Lack of Appropriate Datasets
			13.3.2.2 Reduction in False Positives and False Negatives
			13.3.2.3 Adversarial Machine Learning
			13.3.2.4 Lack of Feature Engineering Techniques
			13.3.2.5 Context-Awareness in Cybersecurity
		13.3.3 Is Machine Learning Enough to Stop Cybercrime?
	13.4 ML Datasets and Algorithms Used in Cybersecurity
		13.4.1 Study of Available ML-Driven Datasets Available for Cybersecurity
			13.4.1.1 KDD Cup 1999 Dataset (DARPA1998)
			13.4.1.2 NSL-KDD Dataset
			13.4.1.3 ECML-PKDD 2007 Discovery Challenge Dataset
			13.4.1.4 Malicious URL’s Detection Dataset
			13.4.1.5 ISOT (Information Security and Object Technology) Botnet Dataset
			13.4.1.6 CTU-13 Dataset
			13.4.1.7 MAWILab Anomaly Detection Dataset
			13.4.1.8 ADFA-LD and ADFA-WD Datasets
		13.4.2 Applications ML Algorithms in Cybersecurity Affairs
			13.4.2.1 Clustering
			13.4.2.2 Support Vector Machine (SVM)
			13.4.2.3 Nearest Neighbor (NN)
			13.4.2.4 Decision Tree
			13.4.2.5 Dimensionality Reduction
	13.5 Applications of Machine Learning in the Realm of Cybersecurity
		13.5.1 Facebook Monitors and Identifies Cybersecurity Threats with ML
		13.5.2 Microsoft Employs ML for Security
		13.5.3 Applications of ML by Google
	13.6 Conclusions
	References
14 Security Improvement Technique for Distributed Control System (DCS) and Supervisory Control-Data Acquisition (SCADA) Using Bl
	14.1 Introduction
	14.2 Significance of Security Improvement in DCS and SCADA
	14.3 Related Work
	14.4 Proposed Methodology
		14.4.1 Algorithms Used for Implementation
		14.4.2 Components of a Blockchain
		14.4.3 MERKLE Tree
		14.4.4 The Technique of Stack and Work Proof
		14.4.5 Smart Contracts
	14.5 Result Analysis
	14.6 Conclusion
	References
15 Recent Techniques for Exploitation and Protection of Common Malicious Inputs to Online Applications
	15.1 Introduction
	15.2 SQL Injection
		15.2.1 Introduction
		15.2.2 Exploitation Techniques
			15.2.2.1 In-Band SQL Injection
			15.2.2.2 Inferential SQL Injection
			15.2.2.3 Out-of-Band SQL Injection
		15.2.3 Causes of Vulnerability
		15.2.4 Protection Techniques
			15.2.4.1 Input Validation
			15.2.4.2 Data Sanitization
			15.2.4.3 Use of Prepared Statements
			15.2.4.4 Limitation of Database Permission
			15.2.4.5 Using Encryption
	15.3 Cross Site Scripting
		15.3.1 Introduction
		15.3.2 Exploitation Techniques
			15.3.2.1 Reflected Cross Site Scripting
			15.3.2.2 Stored Cross Site Scripting
			15.3.2.3 DOM-Based Cross Site Scripting
		15.3.3 Causes of Vulnerability
		15.3.4 Protection Techniques
			15.3.4.1 Data Validation
			15.3.4.2 Data Sanitization
			15.3.4.3 Escaping on Output
			15.3.4.4 Use of Content Security Policy
	15.4 Cross Site Request Forgery
		15.4.1 Introduction
		15.4.2 Exploitation Techniques
			15.4.2.1 HTTP Request with GET Method
			15.4.2.2 HTTP Request with POST Method
		15.4.3 Causes of Vulnerability
			15.4.3.1 Session Cookie Handling Mechanism
			15.4.3.2 HTML Tag
			15.4.3.3 Browser’s View Source Option
			15.4.3.4 GET and POST Method
		15.4.4 Protection Techniques
			15.4.4.1 Checking HTTP Referer
			15.4.4.2 Using Custom Header
			15.4.4.3 Using Anti-CSRF Tokens
			15.4.4.4 Using a Random Value for each Form Field
			15.4.4.5 Limiting the Lifetime of Authentication Cookies
	15.5 Command Injection
		15.5.1 Introduction
		15.5.2 Exploitation Techniques
		15.5.3 Causes of Vulnerability
		15.5.4 Protection Techniques
	15.6 File Inclusion
		15.6.1 Introduction
		15.6.2 Exploitation Techniques
			15.6.2.1 Remote File Inclusion
			15.6.2.2 Local File Inclusion
		15.6.3 Causes of Vulnerability
		15.6.4 Protection Techniques
	15.7 Conclusion
	References
16 Ransomware: Threats, Identification and Prevention
	16.1 Introduction
	16.2 Types of Ransomwares
		16.2.1 Locker Ransomware
			16.2.1.1 Reveton Ransomware
			16.2.1.2 Locky Ransomware
			16.2.1.3 CTB Locker Ransomware
			16.2.1.4 TorrentLocker Ransomware
		16.2.2 Crypto Ransomware
			16.2.2.1 PC Cyborg Ransomware
			16.2.2.2 OneHalf Ransomware
			16.2.2.3 GPCode Ransomware
			16.2.2.4 CryptoLocker Ransomware
			16.2.2.5 CryptoDefense Ransomware
			16.2.2.6 CryptoWall Ransomware
			16.2.2.7 TeslaCrypt Ransomware
			16.2.2.8 Cerber Ransomware
			16.2.2.9 Jigsaw Ransomware
			16.2.2.10 Bad Rabbit Ransomware
			16.2.2.11 WannaCry Ransomware
			16.2.2.12 Petya Ransomware
			16.2.2.13 Gandcrab Ransomware
			16.2.2.14 Rapid Ransomware
			16.2.2.15 Ryuk Ransomware
			16.2.2.16 Lockergoga Ransomware
			16.2.2.17 PewCrypt Ransomware
			16.2.2.18 Dhrama/Crysis Ransomware
			16.2.2.19 Phobos Ransomware
			16.2.2.20 Malito Ransomware
			16.2.2.21 LockBit Ransomware
			16.2.2.22 GoldenEye Ransomware
			16.2.2.23 REvil or Sodinokibi Ransomware
			16.2.2.24 Nemty Ransomware
			16.2.2.25 Nephilim Ransomware
			16.2.2.26 Maze Ransomware
			16.2.2.27 Sekhmet Ransomware
		16.2.3 MAC Ransomware
			16.2.3.1 KeRanger Ransomware
			16.2.3.2 Go Pher Ransomware
			16.2.3.3 FBI Ransom Ransomware
			16.2.3.4 File Coder
			16.2.3.5 Patcher
			16.2.3.6 ThiefQuest Ransomware
			16.2.3.7 Keydnap Ransomware
			16.2.3.8 Bird Miner Ransomware
	16.3 Ransomware Life Cycle
	16.4 Detection Strategies
		16.4.1 UNEVIL
		16.4.2 Detecting File Lockers
		16.4.3 Detecting Screen Lockers
		16.4.4 Connection-Monitor and Connection-Breaker Approach
		16.4.5 Ransomware Detection by Mining API Call Usage
		16.4.6 A New Static-Based Framework for Ransomware Detection
		16.4.7 White List-Based Ransomware Real-Time Detection Prevention (WRDP)
	16.5 Analysis of Ransomware
		16.5.1 Static Analysis
		16.5.2 Dynamic Analysis
	16.6 Prevention Strategies
		16.6.1 Access Control
		16.6.2 Recovery After Infection
		16.6.3 Trapping Attacker
	16.7 Ransomware Traits Analysis
	16.8 Research Directions
	16.9 Conclusion
	References
Index
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