Artificial Intelligence and Blockchain in Digital Forensics
- Length: 264 pages
- Edition: 1
- Language: English
- Publisher: River Publishers
- Publication Date: 2023-02-10
- ISBN-10: 8770226881
- ISBN-13: 9788770226882
- Sales Rank: #0 (See Top 100 Books)
Digital forensics is the science of detecting evidence from digital media like a computer, smart phone, server, or network. It provides the forensic team with the most beneficial methods to solve confused digital-related cases. AI and blockchain can be applied to solve online predatory chat cases and photo forensics cases, provide network service evidence, custody of digital files in forensic medicine, and identify roots of data scavenging. The increased use of PCs and extensive use of internet access, has meant easy availability of hacking tools. Over the past two decades, improvements in the information technology landscape have made the collection, preservation, and analysis of digital evidence extremely important. The traditional tools for solving cybercrimes and preparing court cases are making investigations difficult. We can use AI and blockchain design frameworks to make the digital forensic process efficient and straightforward. AI features help determine the contents of a picture, detect spam email messages and recognize swatches of hard drives that could contain suspicious files. Blockchain-based lawful evidence management schemes can supervise the entire evidence flow of all of the court data.
This book can provide a wide-ranging overview of how AI and blockchain can be used to solve problems in digital forensics using advanced tools and applications available on the market.
Cover Half Title Untitled Series Title Copyright Table of Contents Preface Acknowledgment List of Contributors List of Figures List of Tables List of Abbreviations 1 Digital Forensics Meets AI: A Game-changer for the 4th Industrial Revolution 1.1 Introduction 1.2 Digital Forensics 1.2.1 Growing need for digital forensics 1.2.2 Process of digital forensics 1.2.3 Advantages offered and limitations confronted by digital forensics 1.3 AI and Digital Forensics 1.3.1 Contribution of AI in the realm of digital forensics 1.3.1.1 Knowledge representation 1.3.1.2 Reasoning process 1.3.1.3 Pattern recognition 1.3.1.4 Knowledge discovery 1.3.1.5 Adaptation 1.3.2 Different variants of AI-based digital forensics 1.3.3 AI techniques used by digital forensics investigators 1.3.4 Deep learning tools and techniques helping in the domain of digital forensics 1.4 Latest AI Trends Impacting Digital Forensics 1.4.1 AI has taken a leap from novelty to necessity 1.4.2 Data-driven AI can generate valuable content 1.4.3 Smaller datasets are as amenable as big data 1.4.4 Edge analytics: An upcoming AI trend 1.4.5 Citizen data scientists: The next big thing under AI 1.4.6 AI has an ethical and responsible role in society 1.5 Challenges and the Road Ahead 1.5.1 Key challenges to be addressed 1.5.1.1 Heterogeneity, resulting in lack of standardization 1.5.1.2 AI can be a double-edged sword 1.5.1.3 Privacy-preserving and legitimacy outcry 1.5.2 Road to the future 1.6 Conclusion References 2 Mitigating and Controlling Virtual Addiction Through Web Forensics and Deep Learning 2.1 Introduction 2.2 Internet Addiction (IA) Types 2.2.1 Cyberbullying addiction 2.2.2 Web obligations 2.2.3 Addiction to cyberspace relationships 2.2.4 Anxious searching for content 2.2.5 Gaming addiction 2.2.6 Smartphone mobile app addiction 2.3 Human Behavior Analysis 2.4 Deep Learning’s Relevance to HumanBehavior Prediction 2.5 Forms of online mining 2.5.1 HTML page information extraction 2.5.2 Commonly associated metadata extraction 2.5.3 Customized web usage monitoring 2.6 Web Usage Mining Process 2.7 RNN-based Analysis of Web History Log Data 2.8 Feed-forward Networks Versus RNNs 2.9 RNN Relying on LSTM 2.10 Various Categories of Forensics 2.10.1 Digital forensics 2.10.2 Forensics over networking 2.10.3 Web forensics 2.10.4 Cloud forensics 2.10.5 Mobile forensics 2.10.6 Web browser forensics 2.11 Web Browser Artifacts 2.11.1 Navigation history 2.11.2 Autocomplete data 2.11.3 Cache 2.11.4 Favicons 2.11.5 Browser session storage 2.11.6 Form data 2.12 Analysis of Website Usage History 2.13 Conclusion 2.14 Acknowledgement References 3 Automatic Identification of Cyber Predators Using Text Analytics and Machine Learning 3.1 Introduction 3.1.1 OPI problem definition 3.2 Literature Survey 3.2.1 Cyber predator intent classification 3.3 System Architecture 3.3.1 Chat category 3.3.2 Chat classification 3.4 Experiments 3.4.1 Dataset 3.4.2 Results of phase 1: Chat labelling 3.4.3 Results of phase 2: Chat classification 3.5 Conclusions References 4 CNN Classification Approach to Detecting Abusive Content in Text Messages 4.1 Introduction 4.1.1 Humanity 4.1.2 Abusive harassment on the internet 4.1.3 Learning algorithm 4.2 Literature Survey 4.3 Proposed Methodology 4.3.1 Pre-processing 4.3.2 Feature extraction 4.3.3 Vector space model (VSM) 4.3.3.1 Bag of words 4.3.4 Classifcation methods 4.3.4.1 Support vector machine (SVM) 4.3.4.2 Multilayer perceptron (MLP) 4.3.4.3 Convolutional neural networks (CNN) 4.4 Performance Analysis and Metrics 4.4.1 Precision 4.4.2 Recall 4.4.3 F-measure 4.4.4 Accuracy 4.5 Results and Discussion 4.6 Conclusion References 5 Detection of Online Sexual Predatory Chats Using Deep Learning 5.1 Introduction 5.2 Machine Learning Models to Detect Online Sexual Predatory Chats 5.2.1 Deep learning 5.2.1.1 Recursive neural network 5.2.1.2 Recurrent neural networks 5.2.1.3 Long short-term memory 5.2.1.4 Convolutional neural networks 5.3 Conclusion 5.4 Acknowledgements References 6 Enhancing ATM Security in the Forensic Domain Using Artificial Intelligence 6.1 Introduction 6.2 Literature Survey 6.3 Problem Statement 6.4 Proposed System 6.5 Methodology 6.6 Result and Discussion 6.7 Future Scope 6.8 Conclusion References 7 Network Forensics Architecture for Mitigating Attacks in Software-defined Networks 7.1 Introduction 7.2 Software-defined Networking Planes 7.3 Attacks in Software-defined Networks 7.4 Network Forensics Architecture for Securing an SDN 7.4.1 Identification phase 7.4.2 Data collection phase 7.4.3 Analysis phase 7.4.3.1 Detection of flooding attack 7.4.3.2 Detection of a flow table overflow attack 7.5 Experimental Analysis 7.5.1 Performance analysis on flooding attack detection 7.5.2 Performance analysis on flow table overflow attack detection 7.6 Conclusion 7.7 Acknowledgement References 8 The Self-destructive Behavioural Effects of Virtual Addiction on Cyber Crime Scene Investigation of Victimless Crimes 8.1 Introduction 8.2 Related Study 8.3 Cognitive Intelligence Role on Addiction Prediction 8.3.1 Self-analysis input 8.3.2 Cognitive intelligence 8.3.3 Symptom validity test 8.3.4 Counselling and medical aids 8.3.5 Self-destructive behaviour 8.4 Causative Factors of Self-destructive Behaviour 8.4.1 Peer pressure 8.4.2 Media advertisements 8.4.3 Society and family 8.4.4 Context for consistent addiction behaviour 8.4.5 Internal signals 8.4.6 Cognitive rewards 8.5 Victimless Addiction Crimes 8.5.1 Gaming addiction 8.5.2 Suicidal attempt crimes 8.5.3 Social media addiction 8.5.4 Gambling addiction 8.5.5 E-commerce addiction 8.6. Virtual Addiction Crimes 8.7 Limitations and Future Directions 8.8 Conclusion 8.9 Acknowledgements References 9 The Future of Artificial Intelligence in Digital Forensics: A Revolutionary Approach 9.1 Introduction 9.2 Artificial Intelligence in Digital Forensics 9.2.1 Applications of AI in DF 9.2.1.1 Data discovery and recovery 9.2.1.2 Device triage 9.2.1.3 Analyze traffc on a network 9.2.1.4 Encrypted information forensics 9.2.1.5 Event restoration 9.2.1.6 Forensics of multimedia 9.2.1.7 Fingerprinting 9.2.2 Challenges of AI in DF 9.2.2.1 Unexplainability of AI 9.2.2.2 AI Anti-forensics 9.2.2.3 Disconnect between the cyber forensics and AI communities 9.2.3 Future of AI for DF 9.2.3.1 Changes to DF examiners 9.2.3.2 Wait times 9.2.3.3 Management of the case 9.2.3.4 XAI for assistance with investigations 9.3 Conclusion References 10 Blockchain Based Digital Forensics:A Fundamental Perspective 10.1 Introduction 10.2 IoT Forensics 10.3 Incident Response 10.4 Chain of Custody 10.4.1 Challenges 10.5 Practical Considerations 10.6 Concluding Remarks 10.7 Acknowledgements References 11 Digital Forensics Identity to Improve Transparency in Block Chain Technology Using Artificial Intelligence 11.1 Introduction 11.1.1 Digital forensics 11.1.2 Standards used to practice digital forensics 11.1.3 Summarization of the challenges in existing digital forensics investigation as depicted in figure 11.3 11.1.4 Blockchain 11.1.5 Artificial intelligence 11.1.6 Internet of things(IOT) 11.2 Literature Survey 11.3 Role of IOT and Blockchain to Improve Transparency in Forensics 11.4 Framework of Digital Forensics 11.5 Proposed System 11.6 Hardware and Network Challenges in Digital Forensics 11.7 Conclusion References 12 Forensic Analysis of Online Social Network Data in Crime Scene Investigation 12.1 Introduction 12.2 Crime Analysis of Online Social Networks 12.2.1 Digital forensics status around the globe – 2021 12.2.2 Social networking sites 12.2.2.1 Statistics on social media 12.2.2.2 Various types of social networking sites 12.2.3 Social media crimes 12.2.4 Digital evidence analysis 12.2.4.1 Analytical purpose 12.2.5 Overview of the digital forensics environment 12.3 The Research Design Behind Forensics 12.4 Crime Investigation/Terror Network Structure 12.5 Mobile Forensics 12.6 Conclusion References 13 Blockchain-based Privacy Preservation Technique for Digital Forensics Records 13.1 Introduction 13.2 Background 13.3 Literature review 13.4 Blockchain-based Privacy Preservation Technique 13.4.1 Information transaction 13.4.2 Smart contract life cycle, user roles, and permissions 13.4.3 Interplanetary file system 13.5 Performance Analysis 13.6 Conclusion References 14 Multilevel Consensus Blockchain Algorithm for Digital Forensics on Medical Data During the COVID 19 Situation 14.1 Introduction 14.2 Literature Review 14.3 Problems 14.4 Methodology 14.5 Conclusion 14.6 Further Work References 15 Blockchain-based Identity Management Systems in Digital Forensics 15.1 Introduction 15.1.1 Digital identity management (DIM) 15.1.2 Digital forensics 15.1.2.1 Uses of digital forensics 15.1.3 Blockchain technology 15.2 Blockchain in DIM 15.2.1 Decentralized identifer (DID) 15.3 Use Cases of DID 15.3.1 Self-sovereign identity (SSI) 15.3.2 Data monetization 15.3.3 Data portability 15.4 Benefts of DID 15.4.1 Decentralized public key infrastructure (DPKI) 15.4.2 Decentralized storage 15.4.3 Manageability and control 15.5 Blockchain and Digital Forensics 15.5.1 Hyperledger composer 15.5.2 Secure forensic model 15.5.2.1 Actors 15.5.2.2 Evidence module 15.5.2.3 Blockchain network 15.5.2.4 Secure Storage 15.6 Performance Evaluation 15.6.1 Throughput 15.6.2 Latency 15.6.3 CPU utilization 15.6.4 Memory utilization 15.6.5 Gas 15.7 Summary References Index About the Editors
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