Advanced Smart Computing Technologies in Cybersecurity and Forensics

✍ Scribed by Keshav Kaushik (editor), Shubham Tayal (editor), Akashdeep Bhardwaj (editor), Manoj Kumar (editor)

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 258
• Edition: 1
• Category: Library

✦ Synopsis

This book addresses the topics related to artificial intelligence, internet of things, blockchain technology, and machine learning and brings together researchers, developers, practitioners, and users interested in cybersecurity and forensics. The first objective is to learn and understand the need and impact of advanced cybersecurity and forensics and how it is implemented with multiple smart computational technologies. This objective will answer why and how cybersecurity and forensics have evolved themselves as one of the most promising and widely accepted technology globally and has widely accepted applications. The second objective is to learn how to use advanced cybersecurity and forensics practices to answer many computational problems where confidentiality, integrity, and availability are essential aspects to handle and answer. This book is structured in such a way so that the field of study is relevant to each reader’s major or interests. The book aims to help each reader see the relevance of cybersecurity and forensics to their career or interests. This book intends to encourage researchers to develop novel theories to enrich the scholar’s knowledge to achieve sustainable development and foster sustainability. The readers will gain valuable knowledge and insights about smart computing technologies using this interesting book.

• Includes detailed applications of cybersecurity and forensics for real-life problems

• Addresses the challenges and solutions related to the implementation of cybersecurity in multiple domains of smart computational technologies. Includes the latest trends and areas of research in cybersecurity and forensics

• Offers both quantitative and qualitative assessments of the topics Includes case studies that will be helpful for the researchers

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgments
Chapter 1: Detection of Cross-Site Scripting and Phishing Website Vulnerabilities Using Machine Learning
1.1 Introduction
1.2 Related Work
1.3 Implementation
1.4 Phishing Websites Detection
1.4.1 Phishing Websites
1.4.2 Phishing Websites Detection Techniques
1.5 Implementation Flowchart ( Figure 1.2)
1.5.1 Dataset
1.5.2 Classifiers
1.6 Result and Discussion
1.7 Conclusion and Future Work
References
Conferences
Online Documents/Resources
Chapter 2: A Review: Security and Privacy Defensive Techniques for Cyber Security Using Deep Neural Networks (DNNs)
2.1 Introduction
2.1.1 Pixel Restoration
2.1.2 Deep Dreaming
2.1.3 Image–Language Translations
2.1.4 Virtual Assistants
2.1.5 Fraud Detection
2.1.6 Automatic Handwriting
2.1.7 Healthcare
2.2 Related Work
2.3 Deep Learning Models for Cyber Security
2.3.1 Convolutional Neural Networks (Conv Nets)
2.3.2 Recurrent Neural Networks (RNNs)
2.3.3 Generative Adversarial Networks (GANs)
2.4 Cyber Attacks and Threats with Deep Neural Network
2.5 Conclusion
References
Chapter 3: DNA-Based Cryptosystem for Connected Objects and IoT Security
3.1 Introduction
3.2 Related Works
3.3 Theory and Background
3.3.1 Cryptography
3.3.2 DNA-Based Cryptography
3.3.3 Huffman Compression
3.4 Proposed Cryptosystem-Based DNA
3.4.1 Specifications Presentation
3.4.2 Encryption Process
3.4.2.1 Consideration for the Key Generation
3.4.2.2 Phases of Encryption Process
3.4.3 Decryption Process
3.4.4 Security Evaluation
3.4.4.1 Frequency Analysis
3.4.4.2 Encryption Key Security Analysis
3.4.4.3 Entropy of the Encryption Key
3.5 Cryptosystem Hardware Implementation
3.5.1 General Description of the Cryptosystem
3.5.2 Presentation of Used Components
3.5.2.1 Temperature and Humidity Sensor DHT11
3.5.2.2 Communication Radio Module NRF24L01
3.5.2.3 Mounting Principle (Transmitter/Receiver)
3.6 Human-Machine Interface (HMI)
3.6.1 Transfer of Data Acquired by Sensors
3.6.2 Visual Programming of the HMI
3.6.2.1 Splitting Data
3.6.2.2 Temperature Display
3.6.3 HMI Visualization
3.7 IoT-Based Supervision
3.7.1 FRED (Front End for Node-Red)
3.7.2 Visualization of HMI on the Cloud
3.8 Conclusion and Future Work
Acknowledgment
References
Chapter 4: A Role of Digital Evidence: Mobile Forensics Data
4.1 Introduction
4.1.1 Technology as Digital Evidence
4.1.2 Digital Forensic
4.2 Related Works
4.3 Mobile Device Forensics
4.3.1 Types of Data Acquisition
4.4 Various Types of Mobile Evidence
4.4.1 SMS/MMS
4.4.2 Call Logs
4.4.3 Multimedia Data
4.4.4 Geolocation
4.4.5 Browser History
4.4.6 Device Application
4.5 Forensics Acquisition and Examination
4.5.1 Creating Social Context
4.5.2 Data Analysis of Call Logs, Chat Communication, and Emails
4.5.3 Pre-Processing
4.5.4 Evaluation
4.6 Conclusion
References
Chapter 5: Analysis of Kernel Vulnerabilities Using Machine Learning
5.1 Introduction
5.1.1 Types of Kernels
5.2 Common Vulnerability Exposure
5.2.1 Common Vulnerability Scoring System (CVSS)
5.2.2 Base Metrics
5.2.3 Temporal Metrics
5.2.4 Environmental Metrics
5.3 Base Metric Group
5.3.1 Scoring
5.3.2 Base Metrics Vulnerability Components
5.3.2.1 Exploitability Metrics
5.3.2.1.1 Attack Vector (AV)
5.3.2.1.2 Attack Complexity (AC)
5.3.2.1.3 Privileges Required (PR)
5.3.2.1.4 User Interaction (UI)
5.3.2.1.5 Scope (S)
5.3.3 Impact Metrics
5.4 Kernel Vulnerabilities
5.4.1 Top Five Linux Vulnerabilities
5.4.2 Microsoft Windows Kernel Vulnerabilities
5.4.3 List of Some Android Kernel Vulnerabilities
5.4.4 Top 10 “Most Commonly Exploited Kernel Vulnerabilities”
5.5 Machine Learning
5.5.1 Types of Machine Learning
5.5.2 Random Forest
5.5.3 Random Forest Regression
5.6 Methodology Adopted and Data Set Used
5.7 Implementation and Analysis Results
5.8 Conclusion
References
Chapter 6: Cyber Threat Exploitation and Growth during COVID-19 Times
6.1 Introduction
6.2 A Web-Mesh Host That Is Trusted
6.3 Our Contributions
6.4 Related Work
6.5 Accumulative Cyber-surveillance Gap During COVID-19
6.6 Cybercrime Epidemic in COVID-19
6.6.1 Secure Virtual Web-Mesh
6.6.2 Protecting Virtual Work Data
6.6.3 Ambush Archetype Overview
6.6.4 COVID-19 and Defense against Phishing
6.6.5 Dealing with an Ambush
6.6.6 Shine a Light on Shadow IT Infrastructure
6.6.7 Access Restrictions Are More Relevant than Ever Before
6.6.8 Keep Up the Controls on Data Loss Protection
6.6.9 Keep the Staff Aware of Risks
6.6.10 Be on Guard for Your Defense Activities
6.6.11 Track the Cyber Hygiene of the Workers
6.6.12 Check the Privileged Users by Sanity
6.7 Proposed Methodology
6.7.1 Improvement in Vicinity Accuracy
6.7.2 Decentralized Architectonic for Infection Tracing
6.7.3 Deep Learning-Based Techniques
6.7.4 Quantum Computing
6.7.5 Quantum Relay
6.7.6 Probabilistic Ambusher
6.8 Data Collection
6.9 Conclusions
References
Chapter 7: An Overview of the Cybersecurity in Smart Cities in the Modern Digital Age
7.1 Introduction
7.2 Smart Cities Concepts
7.2.1 Technological Aspects Applicable to Smart Cities
7.3 The Importance of Cybersecurity in Smart Cities
7.3.1 Security Challenges to Smart City Networks
7.4 Discuss
7.5 Trends
7.6 Conclusions
References
Chapter 8: The Fundamentals and Potential for Cyber Security of Machine Learning in the Modern World
8.1 Introduction
8.2 IoT Concept
8.2.1 IoT Aspects Security
8.3 Machine Learning Concept
8.3.1 Types of Learning
8.3.2 Deep Learning
8.4 Discuss
8.5 Trends
8.6 Conclusions
References
Chapter 9: Qualitative and Quantitative Evaluation of Encryption Algorithms
9.1 Introduction
9.2 Encryption
9.2.1 Symmetric Encryption Systems
9.2.2 Asymmetric Encryption Systems
9.2.3 What Makes Them Strong?
9.3 Algorithms Under Consideration
9.3.1 Advanced Encryption Standard
9.3.2 3-DES – Triple Data Encryption Standard
9.3.3 RSA – Rivest-Shamir-Aldelman
9.3.4 TwoFish
9.3.5 Blowfish
9.4 Ranking Formula
9.5 Quantitative Observations
9.6 Qualitative Analysis vs. Numbers
9.7 Composition of Results
9.8 Conclusions
9.9 Limitations and Future Work
References
Chapter 10: Analysis and Investigation of Advanced Malware Forensics
10.1 Introduction to Malware
10.1.1 Definition
10.2 Malware Analysis
10.2.1 Types of Exploration
10.2.2 Platforms of Malware Study
10.2.3 Malware Attacks
10.3 Malware Forensics
10.3.1 Advanced Malware
10.3.2 Memory Forensics
10.3.3 Case Study 1: Rationalization the Assortment and Results [ 5 ]
10.3.4 Case Study 2: Fraudster Tries to Access Client’s Super Funds after Email Hacked
10.4 Malware Forensics Tools
10.4.1 Static or Basic Analysis Tools
10.4.2 Dynamic Analysis Tools
10.5 Procedure Monitor
10.5.1 Open-Source Malware Forensics Tools
10.5.1.1 Example of Advanced Malware (APT)
10.6 Conclusions
References
Chapter 11: Network Intrusion Detection System Using Naïve Bayes Classification Technique for Anomaly Detection
11.1 Introduction
11.2 Literature Review
11.2.1 Naïve Bayes Classification Data Mining Technique
11.2.2 Networking Attacks
11.3 Research Methodology
11.3.1 Intrusion Detection Methodologies
11.3.2 Knowledge Discovery in Databases (KDD) Cup 1999 Dataset Methodologies
11.3.3 Attributes of KDD’ Cup 1999 Dataset
11.3.4 Symbolic Features of KDD’ Cup 1999 Dataset
11.3.5 Numerical Features of KDD’ Cup 1999 Dataset
11.4 Results
11.4.1 Analysis
11.4.2 Findings
11.5 Discussion
11.6 Conclusion
11.7 Recommendations and Future Research
References
Chapter 12: Data Security Analysis in Mobile Cloud Computing for Cyber Security
12.1 Introduction
12.2 Mobile Cloud Computing and Its Challenges
12.3 Literature Review
12.4 Research Methodology
12.4.1 Participants
12.4.2 Materials
12.4.3 Procedures
12.4.4 Results
12.5 Data Security in Mobile Cloud Computing
12.6 Discussion
12.7 Conclusion
References
Chapter 13: A Comprehensive Review of Investigations of Suspects of Cyber Crimes
13.1 Introduction
13.2 Definitions of Key Terms
13.3 Related Techniques
13.3.1 Interrogations of Suspects of Cyber Crimes
13.3.2 Questioning Suspects of Cyber Crimes
13.3.3 The Legal and Technical Challenges with Interrogations of Suspects of Cyber Crimes
13.4 A Model for Classifying Suspects of Cyber Crimes
13.4.1 A Model for Conducting Preliminary Examinations of Suspects of Cyber Crimes
13.4.2 Litigation of Suspects of Cyber Crimes
13.4.3 Managing the Suspects of Cyber Crimes
13.4.4 Discharging an Insider that Is Suspected of Cyber Crime
13.5 A Model for Classifying Witness to Cyber Crimes
13.5.1 Threats to Witness to Cyber Crime
13.5.2 Dismissal of Cyber Lawsuit
13.6 Conclusion
13.6.1 Suggestions
13.6.2 Future Research
References
Chapter 14: Fault Analysis Techniques in Lightweight Ciphers for IoT Devices
14.1 Security in IoT Environments
14.2 Lightweight Ciphers for IoT Systems
14.3 Design Constraints for Hardware Implementations
14.4 Design Constraints for Software Implementations
14.5 Communication Protocols
14.6 Crypto-Primitives with Lightweight Design
14.7 Addition-Rotation-XOR (ARX)-Based Ciphers
14.8 Ultralightweight Cryptography
14.9 IoT Cryptography
14.10 Key Schedule Operation
14.11 FA Attacks
14.11.1 DFA
14.11.2 Fault Sensitivity Analysis (FSA)
14.11.3 Differential Fault Intensity Analysis (DFIA)
14.11.4 SEA and DBA
14.12 Fault Injection Methodologies: Semi-Invasive and Non-Invasive Methods
14.12.1 Power Surge
14.12.2 Clock Glitch
14.12.3 Laser Injection
14.12.4 Electromagnetic Injection
14.13 Types of Faults
14.14 Fault Models
14.15 Countermeasures to Mitigate FA Attacks
14.16 Conclusion
References
Index