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.
Author(s): P. Karthikeyan, Hari Mohan Pandey, Velliangiri Sarveshwaran
Series: River Publishers Series in Digital Security and Forensics
Publisher: River Publishers
Year: 2022
Language: English
Pages: 296
City: Gistrup
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
