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Android and IOS Mobile Forensics: Leveraging Blockchain, Machine Learning, and Deep Learning for Digital Investigations

Name: Android and IOS Mobile Forensics: Leveraging Blockchain, Machine Learning, and Deep Learning for Digital Investigations
ISBN: 9798868817489

by Ravi Sheth, Keshav Kaushik, Chandresh Parekha, Narendrakumar Chayal

January 2026

Intermediate to advanced

495 pages

12h 18m

English

Apress

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Android and IOS Mobile Forensics
Table of Contents
About the Authors
About the Technical Reviewer
1. Introduction to Mobile Forensics
Evolution of Mobile ForensicsIntroduction to Mobile ForensicsWhy Do We Need Mobile ForensicsMobile Forensics ApproachesThe Mobile Forensic Data Extraction/Acquisition MethodsMobile Phone Evidence Extraction and Digital Investigation ProcessForensic Examination of Digital Evidence—A Guide for Law EnforcementGeneral guidelinesEvidence GuidelinesForensic Good PracticesChallenges of Mobile ForensicsSummary
2. Android Forensics Fundamentals
Android Device ArchitectureOverview of Android System ComponentsLinux KernelTypes of Data on Android DevicesUser DataSystem DataApp DataLocations of Key ArtifactsForensic Acquisition MethodsPhysical AcquisitionLogical AcquisitionFile System AcquisitionChallenges in AcquisitionForensic Tools and TechniquesOverview of Popular Forensic ToolsSteps for Logical, Physical, and File System Acquisition for Android MobileData Recovery TechniquesHandling Encrypted DataAndroid File System and App AnalysisFile Systems Used in AndroidAnalyzing Android Package Files (APKs) and App-Specific DataKey Tools for File and App AnalysisSummary
3. iOS Forensics Fundamentals
iOS Device ArchitectureIntroduction to iOS System ComponentsSecure Enclave and EncryptionRuntime on iOS and App SandboxingSystem Security and PermissionsTypes of Data on iOS DevicesUser DataPlaces of Key Artifacts in iOSForensic Acquisition MethodsPhysical Acquisition MethodLogical AcquisitionFile System AcquisitionCloud-Based AcquisitionAcquisition Challenges in iOS DevicesForensic Tools and TechniquesProcess to Be Followed for the Data AcquisitioniCloud Data AcquisitionAccess iCloud AccountAnalyzing iCloud/iOS BackupsData Recovery and Decryption TechniquesCommon Steps for Both Jailbreaking and Passcode BypassiOS File System and App AnalysisiOS File Systems: APFS and HFS+Analyzing App Data and iOS Apps (IPA Files)Tools for iOS File and App AnalysisSummary
4. Leveraging Blockchain for Mobile Forensics
Introduction to BlockchainBlockchain Applications for Mobile DeviceAxie InfinityPancakeSwapUniswapCompoundOpenSeaBlockchain-Related CybercrimesCase 1: The DPRK’s DMM Bitcoin ExploitCase 2: Gainbitcoin Cryptocurrency Scam—Multilevel Marketing SchemeForensic Analysis of Blockchain Data on Mobile DevicesKey Concepts in Blockchain Forensic Analysis on Mobile DevicesSteps for Forensic Analysis of Blockchain Data on Mobile DevicesTools for Blockchain Forensic Analysis on Mobile DevicesKey Blockchain-Related Artifacts on Mobile DevicesChallenges in Blockchain Forensic Analysis on Mobile DevicesDecentralized Application and InvestigationWhat Are Decentralized Applications (dApps)?Investigating Crimes Involving dAppsCryptocurrency and Wallet InvestigationsUnderstanding Cryptocurrency WalletsTypes of WalletsHow to Identify the WalletInvestigating Cryptocurrency TransactionsWallet AnalysisTechniques for Linking Wallets to IndividualsChallenges in Blockchain Mobile ForensicsPseudonymity and AnonymityDecentralized Nature of BlockchainData Encryption and Privacy FeaturesVolume and Complexity of DataOpportunities in Blockchain Mobile ForensicsTransparent and Immutable DataIntegration with Mobile Forensic ToolsSmart Contract and dApp InvestigationReal-Time Monitoring of Blockchain TransactionsSummary
5. Investigating Mobile Banking and Financial Applications
The Evolution of Mobile-Based ApplicationsSecurity Features of Banking ApplicationCyber Crimes and Fraud Associated with Mobile BankingMalware CategoriesForensic Evidences and Acquisition of Mobile DevicesMethods of Mobile Forensics AcquisitionPhysical AcquisitionPhysical Acquisitions ProcessExamine the Collected DataLogical AcquisitionFull File System Mobile ExtractionTimeline AnalysisCommon Data Sources for Analysis for Banking Application InvestigationExamine the Timeline for Trends and InsightsExamination of Mobile Banking Data or Malicious Applications Using a Case StudyDynamic Analysis ProcessIdentifying Behavior of ApplicationIPhone MalwareSummary
6. Examination of Social Media and Messaging Applications
Types of Data Generated by Social Media and Messaging ApplicationsForensic Acquisition Methods for Social Media and Messaging Data from Desktop/Laptop or MobileForensic Acquisition ProcedureForensic Imaging/Cloning of a DriveForensic Artifacts or EvidenceInternet Browsing HistoryPrefetch DocumentsRestoration of Erased Social Media ArtifactsSocial Media Artifacts from Volatile MemoryTools for Acquiring Memory DumpsWhy RAM Dumps Are Important for Social Media ForensicsCommon Social Media Artifacts Identified in Memory DumpsStandard Methods for Analyzing RAM DumpsTools for Analyzing RAM DumpsExtraction of Social Media Artifacts from Mobile DevicesSocial Media Artifacts from the CloudCloud Storage, Synchronization, and ForensicsCommon CharacteristicsChallenges in Cloud Storage InvestigationCloud Storage Forensics: Methods for Acquiring Digital EvidenceForensic Tools for Data Retrieval and ExaminationForensics Investigation Challenges in Social Media and Instant Messaging AppsSummary

7. Location-Based Data Analysis and Geolocation Artifacts
What Is Location-Based Data?Geolocation ArtifactsForensic Methods for Collecting and Analyzing Geolocation DataLocation Data Storing Path from Android and IOS DeviceLocation Data from Social Media ApplicationsIP Address GeolocationForensic Challenges in Analyzing Geolocation DataAnalysis Techniques for Geolocation ArtifactsPrivacy and Security ImplicationsData Security RisksEthical Issues in Geolocation Data AnalysisSummary
8. Mobile Device Network and Forensics
Overview of Mobile Networks and the Importance of Forensic Analysis in Mobile Network EnvironmentsIntroduction to Mobile NetworksThe Growing Complexity and Security of Mobile NetworksImportance of Mobile Device Network and ForensicsChallenges of Mobile Network ForensicsMobile Network Architecture (3G/4G/Wi-Fi/Bluetooth)Mobile Networks Overview: Cellular Network Architecture3G and 4G NetworksMobile Wi-Fi NetworksMobile Wi-Fi Network StructureMobile Wi-Fi Traffic and Forensic CollectionMobile Bluetooth Networks and Forensic CollectionCapturing Network Traffic from Mobile Devices and InfrastructurePacket SnifferNetwork Tap DevicesTools and Software for Traffic CaptureCapturing Infrastructure-Level TrafficTypes of Network Forensic Artifacts in Mobile DevicesLog FilesIP AddressSubscriber Identity Module (SIM) and International Mobile Subscriber Identity (IMSI) TracingEvidence PreservationMobile Network Attacks and ThreatsCommon Mobile Network ThreatsAdvanced Persistence Threats (APTs)Case Study: High-Profile AttacksIncident Response and InvestigationSummary
9. Mobile Browser Forensics
Introduction to Mobile Browser ForensicsOverview of Mobile Browser Forensics and Digital Investigation RelevanceCommon Mobile Browsers and PlatformsImportance in Criminal, Civil, and Corporate InvestigationsForensic Acquisition Challenges: Sandboxing, Encryption, and PermissionsAnalysis of Browser ArtifactsTypes of Browser ArtifactsArtifact Storage Paths on Android and iOSVolatile vs. Persistent ArtifactsTools and Methods for ExtractionRecovery of Deleted DataTechniques for Browser Data RecoveryChallenges in Recovering Incognito or Cleared HistoryFile System Behavior and Data RemanenceIdentification of Web-Based ThreatsMobile Browser Threat VectorsArtifact-Based Threat DetectionIndicators of Compromise (IoCs)Timeline Correlation of Malicious BehaviorCross-Browser Forensic AnalysisThe Need for Multi-browser CorrelationStructural and Encryption DifferencesCross-Browser Behavioral ProfilingChallenges in Dataset MergingConclusion
10. Leveraging Machine Learning for Mobile Forensics
Machine Learning (ML) Techniques for Mobile Digital InvestigationsSupervised LearningUnsupervised LearningSemi-supervised LearningReinforcement LearningData Preparation and Feature EngineeringData CollectionData CleaningNoise Handling TechniquesHandling Missing ValuesFeature ExtractionIdentification of Relevant Features from Raw Data IncludesDimensionality Reduction TechniquesFeature SelectionMethods of Feature SelectionMachine Learning Models and AlgorithmsOverview of Common ModelsModel Selection CriteriaNature of DataTask TypeOther FactorsTraining Machine Learning ModelsData SplittingTraining MethodologiesOverfitting and UnderfittingOverfittingUnderfittingFine-Tuning and OptimizationHyperparameter Tuning TechniquesEarly StoppingRegularization TechniquesEvaluation and Validation of Machine Learning ModelsApplications of Machine Learning in Mobile Digital InvestigationsIntegration of Machine Learning with Forensic Tools and ProcessesWorkflow IntegrationChallenges and LimitationsSummary
11. Applications of Machine Learning in Mobile Forensics
Machine Learning Models for Anomaly Detection in Mobile PhonesCommon Mobile Data AnomaliesSupervised Learning for Anomaly DetectionUnsupervised Learning for Anomaly DetectionSemi-supervised Learning for Anomaly DetectionMachine Learning Approaches Predictive AnalysisUse of Predictive Analysis in Mobile ForensicsAppropriate Machine Learning Model for Predictive AnalysisFinancial Fraud DetectionMachine Learning Models for Fraud DetectionSocial Media Forensics in Mobile PhonesMachine Learning Methods for Analyzing Social Media DataInsights and Value from Social Media AnalyticsThreat Intelligence in Mobile PhonesMachine Learning Roles in Enhancing Threat Detection in Mobile PhonesEmail Forensics in Mobile PhonesMachine Learning Techniques for Emails ForensicsExample of Email Classification in Forensic InvestigationsCase StudySummary
12. Deep Learning Techniques for Mobile Forensics
Introduction to Deep Learning in Mobile ForensicsScope, Relevance, and Urgency of Mobile Forensics in the Digital AgeHow Deep Learning Surpasses Traditional Rule-Based MethodsUse Cases Where Manual Forensic Approaches Fail or Scale PoorlyHigh-Volume Data ProcessingMultimedia Evidence AnalysisBehavioral Profiling and Anomaly DetectionEvasion and Obfuscation TacticsDynamic and Cloud-Based ArtifactsDigital Evidence Extraction Using Deep LearningTypes of Digital Evidence on Mobile DevicesRole of CNNs, RNNs, and Transformers in Evidence ParsingConvolutional Neural Networks (CNNs)Recurrent Neural Networks (RNNs)TransformersFrom Raw Data to Labeled Evidence: The Deep Learning PipelineData AcquisitionData PreprocessingFeature RepresentationModel Training or InferenceEvidence Classification and LabelingDeep Learning Models and Applications in Mobile ForensicsOverview of Deep Learning Models in Mobile ForensicsMobile Malware DetectionMalicious URL DetectionUser Behavior AnalysisMobile Network Traffic ClassificationWeb/Browser Threat IdentificationIntegration into Forensic WorkflowsMultimodal Data Analysis for ForensicsThe Need for Multimodal Analysis in Mobile ForensicsReal-World Examples of Multimodal AnalysisDeep Learning Models for Multimodal FusionEarly FusionLate FusionEmerging Models for Multimodal Mobile ForensicsChallenges and Future DirectionsConclusionConclusion and Future ScopeSuperiority and Applicability in Mobile ForensicsLegal and Ethical ChallengesExplainability and TransparencyPrivacy ImplicationsForensic Admissibility and Legal StandardsFuture Scope: Emerging Trends and InnovationsFederated ForensicsOn-Device Forensic AnalysisPrivacy-Preserving Deep ModelsCross-Domain Intelligence and Multilingual ModelsClosing Remarks
13. Privacy and Security Considerations in Mobile Forensics
Privacy and Security in Mobile ForensicsSecurity Concerns in Mobile ForensicsChallenges in Mobile ForensicsChain of Custody and Data IntegrityChain of Custody in Forensic InvestigationsData IntegrityBest Practices for Documenting and Preserving the Chain of Custody and Maintaining Data IntegrityGood Practices on Recording and Maintaining Chain of CustodyGood Practices for Maintaining Data IntegrityMobile Devices and Issues Within Chain of Custody and Integrity of DataRole of Consent in ForensicsPrivacy Laws and Users’ RightsLaws Affect Consent in InvestigationsInformed ConsentExceptions to ConsentImpact on InvestigationsForensic Tool Security and Reliability in Mobile ForensicsCriteria for Selecting Secure and Reliable Forensic ToolsTool ReliabilityTesting and CertificationCommon Standards for Forensic Tool Certification and Reliability AssessmentKey Summary
14. Future Trends and Emerging Technologies in Mobile Forensics
IntroductionImportance of Adapting to Rapidly Evolving Mobile TechnologiesLimitations of Traditional Forensic ModelsAim and Scope of This Chapter5G/6G Network ForensicsTechnological Advancements and Forensic PotentialForensic Challenges in Next-Gen NetworksAugmented Reality (AR) and Virtual Reality (VR) ForensicsEvidence Generation in Immersive EnvironmentsAnalytical and Reconstruction TechniquesCross-Domain Collaboration and Information SharingMulti-stakeholder Forensic WorkflowsPrivacy-Preserving and Auditable ForensicsRoadmap and Future DirectionsTimeline of Key Technological IntegrationsResearch and Policy RecommendationsConclusion
15. Ethical and Legal Frameworks in Mobile Forensics
IntroductionEthical Foundations in Mobile ForensicsLegal Mandates and Evidentiary IntegrityHuman Rights and Public TrustThe Need for an Integrated FrameworkEthical Guidelines for Forensic PractitionersProfessional Conduct and Data IntegrityPrivacy Respect and Minimization PracticesLegal and Regulatory Frameworks While Handling EvidenceJurisdictional Boundaries and Legal AccessChain of Custody and Evidence IntegrityLegal Standards, Continuous Monitoring, and Regulatory ComplianceCompliance with Legal Frameworks and Industry RegulationsMonitoring and Process AuditsConclusion
Index

Content preview from Android and IOS Mobile Forensics: Leveraging Blockchain, Machine Learning, and Deep Learning for Digital Investigations

R. Sheth et al.Android and IOS Mobile Forensicshttps://doi.org/10.1007/979-8-8688-1748-9_7

7. Location-Based Data Analysis and Geolocation Artifacts

Ravi Sheth¹, Keshav Kaushik², Chandresh Parekha¹ and Narendrakumar Chayal³

(1)

Gandhinagar, Gujarat, India

(2)

Greater Noida, Uttar Pradesh, India

(3)

Ahmedabad, Gujarat, India

Advancement in mobile technology has provided various opportunities for the end user to use GPS devices. Nowadays, all smartphones, smart watches, cars, tablets, navigation devices, drones, palmtops, etc., have inbuilt GPS device and they store users’ movement and travel history on their local database and on cloud application. Cars also comes ...

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Android and IOS Mobile Forensics: Leveraging Blockchain, Machine Learning, and Deep Learning for Digital Investigations

by Ravi Sheth, Keshav Kaushik, Chandresh Parekha, Narendrakumar Chayal

7. Location-Based Data Analysis and Geolocation Artifacts

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