Advanced Techniques and Applications of Cybersecurity and Forensics

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
Preface
About the Editor’s
Contributors
Chapter 1: Advanced Cybersecurity Tools and Techniques
	1.1 Introduction
	1.2 Basics of Cybercrime
	1.3 Classification of Cybercrimes
	1.4 Emerging Threats
	1.5 Cybersecurity
	1.6 Types of Cybersecurity
	1.7 Tools and Techniques
		1.7.1 Cybersecurity Tools and Their Classification
		1.7.2 Necessary Cybersecurity Technologies and Methods
		1.7.3 AI-based Tools for Cybersecurity
		1.7.4 Cybersecurity Automation Tools
		1.7.5 Cybersecurity Tool Applications
		1.7.6 Cybersecurity Techniques for Digital Forensic
	1.8 Application of Cybersecurity and Digital Forensics
		1.8.1 Digital Forensics
	References
Chapter 2: Advanced Forensics Open-Source Tools
	2.1 Introduction
	2.2 What Is Digital Forensic?
	2.3 History
	2.4 Objective of Forensic Analysis of Digital Crimes
	2.5 Stages of Investigation of Digital Evidence
	2.6 Digital Forensic Tools
	2.7 Different Tools of Digital Forensic
	2.8 Different Techniques of Digital Forensic
	2.9 Open-Source Tools and Techniques
	2.10 Open-Source versus Proprietary
	2.11 Benefits of Open-Source Tools
	2.12 Classification of Tools and Techniques
		2.12.1 Linux-Based OST
		2.12.2 Windows-Based Open-Source Tools
		2.12.3 Web-Based Open-Source Tools for Digital Forensic
	2.13 Applications of Digital Forensics Open Tools
	Bibliography
Chapter 3: Artificial Intelligence and Machine Learning-Enabled Cybersecurity Tools and Techniques
	3.1 Introduction: Background and Driving Forces
	3.2 Types of Cyber threats
		3.2.1 Malware
		3.2.2 Phishing
		3.2.3 Denial-of-Service (DoS) Attacks
		3.2.4 Man-in-the-Middle Attacks
		3.2.5 SQL Injection
		3.2.6 Cross-Site Scripting
		3.2.7 Advanced Persistent Threats
		3.2.8 Zero-Day Exploits
	3.3 Types of Cybersecurity Solutions
		3.3.1 Network Security
		3.3.2 Endpoint Security
			3.3.2.1 Antivirus and Anti-Malware
			3.3.2.2 Host-Based Firewall
			3.3.2.3 Intrusion Detection and Prevention Systems
			3.3.2.4 Data Loss Prevention
			3.3.2.5 Encryption
			3.3.2.6 Mobile Device Management (MDM)
			3.3.2.7 Patch Management
		3.3.3 Identity and Access Management
			3.3.3.1 Single Sign-On
			3.3.3.2 Multi-Factor Authentication
			3.3.3.3 Role-Based Access Control
			3.3.3.4 Privileged Access Management (PAM)
			3.3.3.5 Identity Governance and Administration
			3.3.3.6 Directory Services
		3.3.4 Data Security
			3.3.4.1 Encryption
			3.3.4.2 Access Control
			3.3.4.3 Backup and Recovery
			3.3.4.4 Data Loss Prevention
			3.3.4.5 Data Classification
			3.3.4.6 Security Awareness and Training
		3.3.5 Application Security
			3.3.5.1 Secure Coding Practices
			3.3.5.2 Penetration Testing
			3.3.5.3 Code Reviews
			3.3.5.4 Security Testing
			3.3.5.5 Access Control
			3.3.5.6 Authentication and Authorization
			3.3.5.7 Encryption
		3.3.6 Cloud Security
			3.3.6.1 Identity and Access Management
			3.3.6.2 Encryption
			3.3.6.3 Network Security
			3.3.6.4 Data Loss Prevention
			3.3.6.5 Compliance
			3.3.6.6 Cloud Provider Security
		3.3.7 Incident Response and Management
			3.3.7.1 Planning and Preparation
			3.3.7.2 Identification
			3.3.7.3 Containment
			3.3.7.4 Investigation and Analysis
			3.3.7.5 Eradication and Recovery
			3.3.7.6 Post-Incident Activities
	3.4 Potential applications of AI and ML in Cybersecurity
		3.4.1 Anomaly Detection
		3.4.2 Threat Intelligence
		3.4.3 User Behavior Analytics
		3.4.4 Malware Detection
		3.4.5 Vulnerability Management
		3.4.6 Predictive Analytics
		3.4.7 Fraud Detection
		3.4.8 Incident Response
	3.5 Potential Requirements of AI and ML in Cybersecurity
		3.5.1 Data Quality
		3.5.2 Expertise
		3.5.3 Computing Resources
		3.5.4 Explainability
		3.5.5 Adversarial Robustness
		3.5.6 Ethical Considerations
	3.6 Benefits of using AI and ML in Cybersecurity
	3.7 Challenges, Limitations, and Possible Future Directions
	3.8 Examples of Practical Usage of AI and ML in Cybersecurity
		3.8.1 Threat Detection
		3.8.2 Malware Detection
		3.8.3 Network Security
		3.8.4 User Behavior Analysis
		3.8.5 Fraud Detection
	3.9 Case Studies
	3.10 Conclusion and Recommendation
	References
Chapter 4: IoT Forensics
	4.1 Introduction
	4.2 IoT and Its Related Devices
	4.3 Importance of IoT Forensics
	4.4 Scope of IoT Forensics
	4.5 IoT Forensics versus Digital Forensics
	4.6 IoT Forensics Process
	4.7 Traditional Digital Forensics versus IoT Forensics
	4.8 Model for Generalised IoT Forensics
	4.9 Investigation Process of IoT Forensics
	4.10 IoT Forensics Tools
	4.11 Opportunities of IoT Forensics
	4.12 Industries Using IoT
	4.13 Smart Homes
	4.14 Requirements for Successful IoT Forensics
	4.15 Challenges and Suggested Solutions for IoT Forensics
	4.16 Open Issues and Future Direction
	4.17 Summary
	References
Chapter 5: Big Data Forensics: Challenges and Approaches
	5.1 Introduction: Background of Big Data
	5.2 History of Big Data
	5.3 Main Properties of Big Data
	5.4 The Value of Big Data and Its Validity
	5.5 Benefits of Big Data
	5.6 Use Cases for Big Data
	5.7 Difficulties When Using Big Data
	5.8 How BD Works
	5.9 Integration
	5.10 Management
	5.11 Analysis
	5.12 Best Practices for Big Data
	5.13 BD Benefits: Aligning Organized and Unstructured Data
	5.14 Forensic on Big data
	5.15 Literature Review: Forensics on Big Data
		5.15.1 Digital Forensics
		5.15.2 Hadoop Distributed File System
		5.15.3 Cloud Services and Forensics on Cloud Big Data
		5.15.4 Big Data Deduplication
	5.16 Future Enhancement
	5.17 Conclusion
	References
Chapter 6: Drone Forensics
	6.1 Introduction
		6.1.1 Unmanned Aerial Vehicles
	6.2 Anatomy of Drone
		6.2.1 Components of Drone
		6.2.2 Flying Mechanism of Drones
		6.2.3 Types of Drones
	6.3 Process of Drone Forensics
		6.3.1 Thematic Depiction of Drone Forensics
		6.3.2 Data Storage in Drones
		6.3.3 Forensics Tools for Drone Forensics
	6.4 Application of Drone in Forensics
	6.5 Challenges of Drone Forensics
	6.6 Limitations of Drone Forensics
		6.6.1 Vulnerabilities in Drone Forensics
		6.6.2 Crime Related to Drones
	6.7 Conclusion
	6.8 Future Aspects
	References
Chapter 7: Ransomware Attacks on IoT Devices
	7.1 Introduction
	7.2 Ransomware
		7.2.1 Evolution of Ransomware
		7.2.2 Classification of Ransomware
		7.2.3 Working Mechanism of Ransomware
	7.3 Internet of Things
	7.4 Ransomware and IoT
		7.4.1 Impact of Ransomware on IoT Devices
		7.4.2 Featuring Prominent Ransomware Attacks on IoT Devices
			7.4.2.1 FLocker
			7.4.2.2 Thermostat Hacking
			7.4.2.3 Attacking Methodology
			7.4.2.4 Simplocker
	7.5 Challenges Involved in Ransomware Detection and Analysis on IoT Devices
	7.6 Techniques to Prevent Ransomware Attacks on IoT
	7.7 Discussion
	7.8 Conclusion
	References
Chapter 8: A Critical Analysis of Privacy Implications Surrounding Alexa and Voice Assistants
	8.1 Introduction
		8.1.1 Using Machine Learning-Based Tools for Detecting and Categorizing Specific Vulnerabilities
		8.1.2 Vulnerability
			8.1.2.1 Types of Vulnerabilities
		8.1.3 Some Basic Terminologies
	8.2 Literature Survey
	8.3 Software Vulnerabilities in Amazon Alexa
		8.3.1 Vulnerabilities Identified in the Amazon Alexa
		8.3.2 Evolution and Variations of Vulnerabilities
		8.3.3 Vulnerabilities by Type
		8.3.4 Vulnerabilities by Year
		8.3.5 Vulnerabilities Trend
		8.3.6 Vulnerabilities in the Years 2022 and 2021
			8.3.6.1 High-Severity Vulnerabilities 2022
			8.3.6.2 CVE-2022-33189
				8.3.6.2.1 Analysis Description
	8.4 Most Prevalent Attacks on Amazon Alexa
		8.4.1 Attack on Alexa Skills
			8.4.1.1 Vulnerability
			8.4.1.2 Mitigation
		8.4.2 Alexa Versus Alexa Attack (AvA)
			8.4.2.1 CVE-2022-25809
			8.4.2.2 Vulnerability
			8.4.2.3 Mitigation
	8.5 Consequences of Security Vulnerabilities in Alexa-Enabled Devices
	8.6 Analysis
	8.7 Conclusion
	References
Chapter 9: Zeus: In-Depth Malware Analysis of Banking Trojan Malware
	9.1 Introduction: Malware
	9.2 Types of Trojan Horse
	9.3 Working of Trojan
	9.4 Popular Trojan Attacks
	9.5 Zeus Trojan
	9.6 Variants of Zeus
	9.7 How Zeus Affects Systems
	9.8 Malware Analysis
	9.9 Preventive Measures for Trojan Attack
	9.10 Additional Measures
	9.11 Conclusion
	References
Chapter 10: Villain: Malware Analysis and Antivirus Evasion of a Backdoor Generator
	10.1 Introduction: Malware and Types of Malware
	10.2 Antivirus Software
		10.2.1 Antivirus Evasion and Types of Antivirus Search Engines
		10.2.2 Static Engine
		10.2.3 File-Based Antivirus Search Engines
		10.2.4 Heuristic-Based Antivirus Search Engines
		10.2.5 Signature-Based Antivirus Search Engines
		10.2.6 Cloud-Based Antivirus Search Engines
		10.2.7 Behavioral-Based Antivirus Search Engines
	10.3 Obfuscation Technique and Anti-Static Obfuscation Techniques
	10.4 Villian Tool: Background of Tool
	10.5 Hoaxshell: Villain Is an Inheritance of Hoaxshell
	10.6 Lab Setup
	10.7 Generation of Payload and Bypassing Antivirus
	10.8 Malware Analysis
	10.9 Conclusion
	References
Chapter 11: An Investigation of Memory Forensics in Kernel Data Structure
	11.1 Introduction
	11.2 Source of Data
	11.3 Literature Review
	11.4 Experimental Setup
	11.5 Conclusion and Future Work
	References
Chapter 12: Analysis and Impacts of Avast Antivirus Vulnerabilities
	12.1 Introduction
		12.1.1 Vulnerability
		12.1.2 Types of Vulnerabilities
		12.1.3 Impact of Vulnerabilities on Software
	12.2 Secure Software Development Life Cycle
	12.3 Vulnerability Database Terminologies
	12.4 Literature Survey
	12.5 Vulnerabilities in Avast Antivirus
		12.5.1 Vulnerabilities by Type
		12.5.2 Vulnerabilities by Year
		12.5.3 Vulnerabilities Trends
		12.5.4 Vulnerabilities in 2022 and 2021
			12.5.4.1 CVE-2022-26522
			12.5.4.2 User Mode
			12.5.4.3 Context Switch and Kernel Mode
			12.5.4.4 CVE-2022-26523
			12.5.4.5 CVE-2022-28965
				12.5.4.5.1 Analysis Description
			12.5.4.6 CVE-2022-28964
				12.5.4.6.1 Analysis Description
			12.5.4.7 2021
			12.5.4.8 CVE-2021-45337
				12.5.4.8.1 Analysis Description
			12.5.4.9 CVE-2021-45336
				12.5.4.9.1 Analysis Description
			12.5.4.10 CVE-2021-45335
				12.5.4.10.1 Analysis Description
		12.5.5 Impact of Avast Antivirus Vulnerabilities
		12.5.6 Operating System Associated with Vulnerability
	12.6 Analysis
	12.7 Results and Conclusion
	References
Chapter 13: How to Recover Deleted Data from SSD Drives after TRIM
	13.1 Introduction
		13.1.1 Background of Solid-State Drive
		13.1.2 Additional Difficulties
			13.1.2.1 How SSDs Erase Data: Even More Difficult
			13.1.2.2 Reserve Cells
			13.1.2.3 Life after TRIM: SSD Technology Mode
			13.1.2.4 Manufacturers Use Technological Mode
		13.1.3 Observations/Results
	13.2 Conclusion
	Acknowledgment
	References
Chapter 14: Early Validation of Investigation Process Model
	14.1 Introduction
	14.2 Types of Cyber Crimes
	14.3 Literature Survey
	14.4 Proposed Work
	14.5 Simulation
	14.6 Analysis of the Work
	14.7 Future Work
	14.8 Conclusion
	References
Chapter 15: Dark Web Forensics
	15.1 Introduction
	15.2 Structure of an Internet
		15.2.1 Surface Web
		15.2.2 Deep Web
		15.2.3 Dark Web
	15.3 Literature Review
	15.4 Comparison of the Dark Web with the Deep Web
	15.5 An Explanation of How to Use TOR to Access an Unindexed Web
	15.6 Working of TOR
	15.7 Computer Forensics and the Methodology Applied
	15.8 How Does the Dark Web Operate with Various Technologies?
	15.9 Features and Applications of Dark Web
		15.9.1 Features Provided by Dark Web
	15.10 Dark Web Applications
		15.10.1 Darknet Markets
		15.10.2 Bitcoin Encouragement
	15.11 Government, Military, and Intelligence using Dark WEB
	15.12 Remittance on the Dark Web
	15.13 Crimes in Dark Web
		15.13.1 Drug Trafficking
		15.13.2 Human Trafficking
		15.13.3 Information Leaks and Theft
		15.13.4 Contract Killers and Murder
		15.13.5 Child Exploitation
		15.13.6 Terrorism
		15.13.7 Using Proxies and Cloning Onions
		15.13.8 Unlawful Financial Activities
		15.13.9 Trafficking in Arms
		15.13.10 Bitcoin Fraud
		15.13.11 Onion Cloning
		15.13.12 Contract Killers
	15.14 Threats on the Dark Web
		15.14.1 Trojans for Stealing Data
		15.14.2 Ransomware
		15.14.3 Remote Access Trojans (RATs)
		15.14.4 Malware Botnet
		15.14.5 Theft Malware
	15.15 Regulatory Challenge
	15.16 Additional Challenges for Organization and Business
		15.16.1 Inaccessible Information
		15.16.2 Mirror and Domain
		15.16.3 Site Instabilities
		15.16.4 Reputation
		15.16.5 CAPTCHA Testing and Manual Anti-Bot Verification
		15.16.6 Cloudflare
		15.16.7 Secure Login
		15.16.8 Paywall
		15.16.9 Paying Invitation
		15.16.10 Hidden Information
	15.17 Approach of Dark Web Framework
	15.18 Dark Web Forensics Technique and Tools
	15.19 Conclusion
	References
Index