Advanced Smart Computing Technologies in Cybersecurity and Forensics
- Length: 264 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-12-16
- ISBN-10: 0367686503
- ISBN-13: 9780367686505
- Sales Rank: #8357056 (See Top 100 Books)
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
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
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