Secure Data Science: Integrating Cyber Security and Data Science

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Acknowledgments
Permissions
Authors
1. Introduction
	1.1 OVERVIEW
	1.2 Big Data Analytics, Data Science, and Machine Learning
	1.3 Supporting Technologies
	1.4 Data Science for Cyber Security
	1.5 Security and Privacy Enhanced Data Science
	1.6 Access Control and Data Science
	1.7 Organization of This Book
	1.8 Next Steps
	Reference
Part I: Supporting Technologies for Secure Data Science
Introduction to Part I
2. Data Security and Privacy
	2.1 Introduction
	2.2 Security Policies
		2.2.1 Access Control Policies
			2.2.1.1 Authorization-based Access Control Policies
			2.2.1.1.1 Positive authorization
			2.2.1.1.2 Negative authorization
			2.2.1.1.3 Conflict resolution
			2.2.1.1.4 Strong and weak authorization
			2.2.1.1.5 Propagation of authorization rules
			2.2.1.1.6 Special rules
			2.2.1.1.7 Consistency and completeness of rules
			2.2.1.2 Role-based Access Control
			2.2.1.3 Usage Control
			2.2.1.4 Attribute-based Access Control
		2.2.2 Administration Policies
		2.2.3 Identification and Authentication
		2.2.4 Auditing a Database System
		2.2.5 Views for Security
	2.3 Policy Enforcement and Related Issues
		2.3.1 SQL Extensions for Security
		2.3.2 Query Modification
		2.3.3 Discretionary Security and Database Functions
	2.4 Data Privacy
	2.5 Summary and Directions
	References
3. Data Mining and Security
	3.1 Introduction
	3.2 Data Mining Techniques
		3.2.1 Overview
		3.2.2 Artificial Neural Networks
		3.2.3 Support Vector Machines
		3.2.4 Markov Model
			3.2.4.1  Example
		3.2.5 Association Rule Mining (ARM)
		3.2.6 Decision Trees
		3.2.7 Multi-class Problem
			3.2.7.1  One-vs-One
			3.2.7.2  One-vs-All
	3.3 Data Mining, Cyber Security, and Privacy
		3.3.1 Cyber Security Threats
			3.3.1.1 Cyber-Terrorism, Insider Threats, and External Attacks
			3.3.1.2 Malicious Intrusions
			3.3.1.3 Credit Card Fraud and Identity Theft
			3.3.1.4 Attacks on Critical Infrastructures
		3.3.2 Data Mining for Cyber Security
		3.3.3 Security and Privacy-Enhanced Data Mining
	3.4 Summary and Directions
	References
4. Big Data, Cloud, Semantic Web, and Social Network Technologies
	4.1 Introduction
	4.2 Big Data Management and Analytics Tools and Technologies
		4.2.1 Infrastructure Tools to Host Big Data Systems
			4.2.1.1 Apache Hadoop
			4.2.1.2 MapReduce
			4.2.1.3 Apache Spark
			4.2.1.4 Apache Pig
			4.2.1.5 Apache Storm
			4.2.1.6 Apache Flink
			4.2.1.7 Apache Kafka
		4.2.2 Big Data Management and Analytics Tools
			4.2.2.1 Apache Hive
			4.2.2.2 Google BigQuery
			4.2.2.3 NoSQL Database
			4.2.2.4 Google BigTable
			4.2.2.5 Apache HBase
			4.2.2.6 MongoDB
			4.2.2.7 Apache Cassandra
			4.2.2.8 Apache CouchDB
			4.2.2.9 Oracle NoSQL Database
			4.2.2.10 Weka
			4.2.2.11 Apache Mahout
	4.3 Cloud Computing
		4.3.1 Cloud Computing Models
			4.3.1.1 Cloud Deployment Models
			4.3.1.2 Service Models
		4.3.2 Virtualization
		4.3.3 Cloud Storage and Data Management
		4.3.4 Cloud Platforms
			4.3.4.1 Amazon Web Services' DynamoDB
			4.3.4.2 Microsoft Azure's Cosmos DB
			4.3.4.3 IBM's Cloud-Based Big Data Solutions
			4.3.4.4 Google's Cloud-Based Big Data Solutions
	4.4 Semantic Web
		4.4.1 Semantic Web Technologies
			4.4.1.1 XML
			4.4.1.2 RDF
			4.4.1.3 SPARQL
			4.4.1.4 OWL
			4.4.1.5 Description Logics
			4.4.1.6 SWRL
		4.4.2 Semantic Web and Security
			4.4.2.1 XML Security
			4.4.2.2 RDF Security
			4.4.2.3 Security and Ontologies
			4.4.2.4 Secure Query and Rules Processing
		4.4.3 Cloud Computing Frameworks Based on Semantic Web Technologies
			4.4.3.1 RDF Integration
			4.4.3.2 Provenance Integration
	4.5 Analyzing and Securing Social Networks
		4.5.1 Introduction to Social Networks
		4.5.2 Social Media Analytics Applications
			4.5.2.1 Applications Extracting Demographics
			4.5.2.2 Sentiment Analysis
			4.5.2.3 Detecting Communities of Interest
			4.5.2.4 Determining Leaders
			4.5.2.5 Detecting Persons of Interest
			4.5.2.6 Determining Political Affiliation
		4.5.3 Data Mining for Social Networks
			4.5.3.1 Association Rule Mining
			4.5.3.2 Classification
			4.5.3.3 Clustering
			4.5.3.4 Anomaly Detection
			4.5.3.5 Web Mining
		4.5.4 Security and Privacy
	4.6 Summary and Directions
	References
5. Big Data Analytics, Security, and Privacy
	5.1 Introduction
	5.2 Issues in Big Data Security and Privacy
		5.2.1 Background for the Workshop
		5.2.2 Big Data Management and Analytics
		5.2.3 Security and Privacy
	5.3 Research Challenges for Big Data Security and Privacy
		5.3.1 Workshop Objectives
		5.3.2 Philosophy for Big Data Security and Privacy
		5.3.3 Examples of Privacy-Enhancing Techniques
		5.3.4 Multi-objective Optimization Framework for Data Privacy
		5.3.5 Multidisciplinary Approaches
		5.3.6 Big Data Analytics for Cyber Security
		5.3.7 Cyber Security for Big Data Analytics
	5.4 Summary and Directions
	References
Conclusion to Part I
Part II: Data Science for Cyber Security
Introduction to Part II
6. Data Science for Malicious Executables
	6.1 Introduction
	6.2 Malicious Executables
		6.2.1 Architecture
		6.2.2 Related Work
		6.2.3 Hybrid Feature Retrieval Model
	6.3 Design of the Data Mining Tool
		6.3.1 Feature Extraction Using n-Gram Analysis
			6.3.1.1 A Binary n-Gram Feature
			6.3.1.2 Feature Collection
			6.3.1.3 Feature Selection
			6.3.1.4 Assembly n-Gram Feature
			6.3.1.5 DLL Function Call Feature
		6.3.2 Details of the Hybrid Feature Retrieval Model
			6.3.2.1 Description of the Model
			6.3.2.2 The Assembly Feature Retrieval (AFR) Algorithm
			6.3.2.3 Feature Vector Computation and Classification
	6.4 Evaluation and Results
		6.4.1 Experiments
		6.4.2 Dataset
		6.4.3 Experimental Setup
		6.4.4 Results
			6.4.4.1 Accuracy
			6.4.4.1.1 Dataset1
			6.4.4.1.2 Dataset2
			6.4.4.1.3 Statistical significance test
			6.4.4.1.4 DLL call feature
			6.4.4.2 ROC Curves
			6.4.4.3 False Positive and False Negative
			6.4.4.4 Running Time
			6.4.4.5 Training and Testing with Boosted J48
		6.4.5 Example Run
	6.5 Big Data Analytics
	6.6 Summary and Directions
	References
7. Stream Analytics for Malware Detection
	7.1 Introduction
	7.2 Stream Mining
		7.2.1 Architecture
		7.2.2 Related Work
		7.2.3 Our Approach
		7.2.4 Overview of Novel Class Detection
		7.2.5 Classifiers Used
	7.3 Details of Novel Class Detection
		7.3.1 Definitions
		7.3.2 Novel Class Detection
			7.3.2.1 Saving the Inventory of Used Spaces during Training
			7.3.2.1.1 Clustering
			7.3.2.1.2 Storing the cluster summary information
			7.3.2.2 Outlier Detection and Filtering
			7.3.2.2.1 Filtering
			7.3.2.3 Detecting Novel Class
			7.3.2.3.1 Computing the set of novel class instances
			7.3.2.3.2 Speeding up the computation
			7.3.2.3.3 Time complexity
			7.3.2.3.4 Impact of evolving class labels on ensemble classification
	7.4 Evaluation
		7.4.1 Datasets
			7.4.1.1 Synthetic Data with Only Concept-Drift (SynC)
			7.4.1.2 Synthetic Data with Concept-Drift and Novel-Class (SynCN)
			7.4.1.3 Real Data - KDD Cup 99 Network Intrusion Detection
			7.4.1.4 Real Data - Forest Cover (UCI Repository)
		7.4.2 Experimental Setup
			7.4.2.1 Baseline Method
		7.4.3 Performance Study
			7.4.3.1 Evaluation Approach
			7.4.3.2 Results
			7.4.3.3 Running Time
	7.5 Security Applications and Malware Detection
	7.6 Summary and Directions
	References
8. Cloud-Based Data Science for Malware Detection
	8.1 Introduction
	8.2 Malware Detection
		8.2.1 Malware Detection as a Data Stream Classification Problem
		8.2.2 Cloud Computing for Malware Detection
		8.2.3 Our Contributions
	8.3 Related Work
	8.4 Design and Implementation of the System
		8.4.1 Ensemble Construction and Updating
		8.4.2 Error Reduction Analysis
		8.4.3 Empirical Error Reduction and Time Complexity
		8.4.4 Hadoop/MapReduce Framework
	8.5 Malicious Code Detection
		8.5.1 Overview
		8.5.2 Non-distributed Feature Extraction and Selection
		8.5.3 Distributed Feature Extraction and Selection
	8.6 Experiments
		8.6.1 Datasets
			8.6.1.1 Synthetic Dataset
			8.6.1.2 Botnet Dataset
			8.6.1.3 Malware Dataset
		8.6.2 Baseline Methods
		8.6.3  Hadoop Distributed System Setup
	8.7 Discussion
	8.8 Summary and Directions
	References
9. Stream Analytics for Insider Threat Detection
	9.1 Introduction
	9.2 Survey of Insider Threat and Stream Mining
		9.2.1 Insider Threat Detection
		9.2.2 Stream Mining
		9.2.3 Big Data Techniques for Scalability
	9.3 Ensemble-Based Insider Threat Detection
		9.3.1 Ensemble Learning
		9.3.2 Ensemble for Unsupervised Learning
		9.3.3 Ensemble for Supervised Learning
		9.3.4 Unsupervised Learning
			9.3.4.1 GBAD-MDL
			9.3.4.2 GBAD-P
			9.3.4.3 GBAD-MPS
	9.4 Insider Threat Detection for Sequence Data
		9.4.1 Classifying Sequence Data
			9.4.1.1 Incremental Learning
			9.4.1.2 Ensemble Learning
			9.4.1.3 Model Update
		9.4.2 Unsupervised Stream-Based Sequence Learning (USSL)
			9.4.2.1 Construct the LZW Dictionary by Selecting the Patterns in the Data Stream
			9.4.2.2 Constructing the Quantized Dictionary
		9.4.3 Anomaly Detection
	9.5 Summary and Directions
	References
Conclusion to Part II
Part III: Security and Privacy-enhanced Data Science
Introduction to Part III
10. Adversarial Support Vector Machine Learning
	10.1 Introduction
	10.2 Related Work
	10.3 Our Approach
	10.4 The Problem and the Attacks
		10.4.1 Problem Definition
		10.4.2 Adversarial Attack Models
			10.4.2.1 Free-range Attack
			10.4.2.2 Restrained Attack
	10.5 Adversarial SVM Learning
		10.5.1 AD-SVM against Free-range Attack Model
		10.5.2 AD-SVM against Restrained Attack Model
	10.6 Experiments
		10.6.1 Our Approach
		10.6.2 Experiments on Artificial Dataset
			10.6.2.1 Data Points Well Separated
			10.6.2.2 Data Cluttered Near Separating Boundary
		10.6.3 Experiments on Real Datasets
		10.6.4 Setting Cf, Cξ, and Cδ
	10.7 Summary and Directions
	References
11. Adversarial Learning Using Relevance Vector Machine Ensembles
	11.1 Introduction
	11.2 Related Work
	11.3 Relevance Vector Machine
	11.4 Kernel Parameter Fitting
		11.4.1 Our Approach
		11.4.2 Kernel Parameter Vector
		11.4.3 Attacks Minimizing the Log-Likelihood
		11.4.4 Training Issues
		11.4.5 Adversarial RVM Learning Algorithm
	11.5 Experimental Results
		11.5.1 Datasets
		11.5.2 Experiments on the Artificial Dataset
		11.5.3 Experiments on Real Datasets
	11.6 Summary and Directions
	References
12. Privacy Preserving Decision Trees
	12.1 Introduction
	12.2 Related Work and Motivation
	12.3 Privacy Metrics
	12.4 Overview of Decision Tree Construction
		12.4.1 Splitting Criterion
		12.4.2 Discretizing Continuous Attributes
		12.4.3 Stopping Criteria
	12.5 Naive Bayes Classifier Construction over Perturbed Data
		12.5.1 Naive Bayes Classifier
		12.5.2 Over Perturbed Numeric Data
	12.6 Privacy Preserving Decision Tree C4.5 (PPDTC4.5)
		12.6.1 Motivation
		12.6.2 Splitting Criterion Using Threshold
		12.6.3 Splitting Training Data by Threshold
		12.6.4 Classifying the Original Instance
		12.6.5 Splitting Criterion
		12.6.6 Splitting Training Data Set Using Random Path Selection
		12.6.7 Classifying the Perturbed Instance Using Random Path Selection
	12.7 Experimental Results
		12.7.1 Our Approach
		12.7.2 Local vs. Global Data Mining
		12.7.3 Reconstruction-Based Approaches Results
		12.7.4 PPDTC4.5 Classifier Accuracy
		12.7.5 Algorithm Complexity
	12.8 Summary and Directions
	References
13. Towards a Privacy Aware Quantified Self Data Management Framework
	13.1 Introduction
	13.2 Privacy-Aware Quantified Self-Data Management Framework
		13.2.1 Motivational scenario
		13.2.2 Architecture Overview
		13.2.3 Related Work
			13.2.3.1 Access Control Policies, Privacy, and Usability
			13.2.3.2 Personal Data Stores
			13.2.3.3 Mining Quantified Self-Data
			13.2.3.4 Differential Privacy and Privacy-Preserving Data Mining
	13.3 High Level Design of The Framework
		13.3.1 Privacy-Aware Data Collection
		13.3.2 Privacy-Aware Data Storage and Access
		13.3.3 Privacy-Aware Data Analytics and Sharing
	13.4 Novel Directions
		13.4.1 Security of User Devices
	13.5 Behavioral Aspects
	13.6 Toward a Formal Framework
	13.7 Summary and Directions
	References
14. Data Science, COVID-19 Pandemic, Privacy, and Civil Liberties
	14.1 Introduction
	14.2 Data Mining, National Security, Privacy, and Civil Liberties
	14.3 Data Science for COVID-19 Detection and Prediction
	14.4 Privacy Implications and Potential Solutions
	14.5 Revisiting the Multi-objective Framework for Data Privacy
	14.6 Role of Confidentiality and Access Control
	14.7 What about Civil Liberties?
	14.8 Balancing Safety and Security vs. Privacy and Civil Liberties
	14.9 Summary and Directions
	References
Conclusion to Part III
Part IV: Access Control and Data Science
Introduction to Part IV
15. Secure Cloud Query Processing Based on Access Control for Big Data Systems
	15.1 Introduction
	15.2 Our Approach
	15.3 Related Work
	15.4 Architecture
		15.4.1 Data Generation and Storage
		15.4.2 File Organization
		15.4.3 Predicate Split
		15.4.4 Split Using Explicit Type Information of Object
		15.4.5 Split Using Implicit Type Information of Object
		15.4.6 Access Control Models
	15.5 MapReduce Framework
		15.5.1 Overview
		15.5.2 Input Files Selection
		15.5.3 Cost Estimation for Query Processing
			15.5.3.1 Ideal Model
			15.5.3.1.1 Map input (MI) phase
			15.5.3.1.2 Map output (MO) phase
			15.5.3.1.3 Reduce input (RI) phase
			15.5.3.1.4 Reduce output (RO) phase
			15.5.3.2 Heuristic Model
		15.5.4 Query Plan Generation
			15.5.4.1 Computational Complexity of Bestplan
			15.5.4.2 Problem Formulation
			15.5.4.3 Search Space Size
			15.5.4.4 Relaxed Bestplan Problem and Approximate Solution
		15.5.5 Breaking Ties by Summary Statistics
		15.5.6 MapReduce Join Execution
	15.6 Results
		15.6.1 Experimental Setup
			15.6.1.1 Datasets
			15.6.1.2 Baseline Frameworks
			15.6.1.3 Hardware
			15.6.1.4 Software
		15.6.2 Evaluation
	15.7 Security Extensions
		15.7.1 Token-Based Access Control Model
		15.7.2 Access Token Assignment
			15.7.2.1 Final output of an Agent's ATs
			15.7.2.2 Security Level Defaults
		15.7.3 Conflicts
		15.7.4 XACML-based Access Control
	15.8 Summary and Directions
	References
16. Access Control-Based Assured Information Sharing in the Cloud
	16.1 Introduction
	16.2 System Design
		16.2.1 Design Philosophy
		16.2.2 Design of CAISS
			16.2.2.1 Enhanced Policy Engine
			16.2.2.2 Enhanced SPARQL Query Processor
			16.2.2.3 Integration Framework
		16.2.3 Limitations of CAISS
		16.2.4 Design of CAISS++
			16.2.4.1 Centralized CAISS++
			16.2.4.2 Decentralized CAISS++
			16.2.4.3 Hybrid CAISS++
			16.2.4.4 Naming Conventions
			16.2.4.5 Vertically Partitioned Layout
			16.2.4.6 Hybrid Layout
			16.2.4.7 Distributed processing of SPARQL
			16.2.4.8 Framework Integration
			16.2.4.9 Policy Specification and Enforcement
		16.2.5 Extensions to the Design
			16.2.5.1 Formal Policy Analysis
			16.2.5.2 Extensions for Big Data-based Applications
	16.3 Implementation Details
		16.3.1 Prototype Implementations
			16.3.1.1 Secure Data Storage and Retrieval in the Cloud
			16.3.1.2 Secure SPARQL Query Processing on the Cloud
			16.3.1.3 Pre-processing
			16.3.1.4 Query Execution and Optimization
			16.3.1.5 RDF Policy Engine
			16.3.1.6 Assured Information Sharing Prototypes
		16.3.2 Other Implementations
		16.3.3 Implementation of the Demonstration Systems
			16.3.3.1 Architecture
			16.3.3.1.1 User interface layer
			16.3.3.1.2 Policy engines
			16.3.3.1.3 Data layer
			16.3.3.2 Features of Our Policy Engine Framework
			16.3.3.2.1 Policy reciprocity
			16.3.3.2.2 Develop and scale policies
			16.3.3.2.3 Justification of resources
			16.3.3.2.4 Policy specification and enforcement
	16.4 Related Work
		16.4.1 Research Efforts
			16.4.1.1 Secure Data Storage and Retrieval in the Cloud
			16.4.1.2 SPARQL Query Processor
			16.4.1.3 RDF-based Policy Engine
			16.4.1.4 Hadoop Storage Architecture
			16.4.1.5 Distributed Reasoning
			16.4.1.6 Access Control and Policy Ontology Modeling
		16.4.2 Commercial Developments
			16.4.2.1 RDF Processing Engines
			16.4.2.2 Semantic Web-based Security Policy Engines
	16.5 Summary and Directions
	References
17. Access Control for Social Network Data Management
	17.1 Introduction
	17.2 Related Work
	17.3 Modeling Social Networks Using Semantic Web Technologies
		17.3.1 Type of Relationships
		17.3.2 Modeling Personal Information
		17.3.3 Modeling Personal Relationships
		17.3.4 Modeling Resources
		17.3.5 Modeling User/Resource Relationships
		17.3.6 Modeling Actions
	17.4 Security Policies for OSNs
		17.4.1 Running Example
		17.4.2 Access Control Policies
		17.4.3 Filtering Policies
		17.4.4 Admin Policies
	17.5 Security Policy Specification
		17.5.1 Policy Language
		17.5.2 Authorizations and Prohibitions
			17.5.2.1 Access Control Authorizations
			17.5.2.2 Prohibitions
			17.5.2.3 Admin Authorizations
		17.5.3 Security Rules
	17.6 Security Rule Enforcement
		17.6.1 Our Approach
		17.6.2 Admin Request Evaluation
		17.6.3 Access Request Evaluation
	17.7 Implementation of an Access Control Framework
	17.8 Framework Architecture
	17.9 Experiments
		17.9.1 Data Generation
	17.10 Scalability with Big Data and Cloud Technologies
	17.11 Summary and Directions
	References
18. Inference and Access Control for Big Data
	18.1 Introduction
	18.2 Design of an Inference Controller
		18.2.1 Architecture
			18.2.1.1 User Interface Manager
			18.2.1.2 Policy Manager
			18.2.1.3 Inference Engine
			18.2.1.4 Data controller
			18.2.1.5 Provenance Controller
	18.3 Inference Control Through Query Modification
		18.3.1 Query Modification
		18.3.2 Query Modification with Relational Data
			18.3.2.1 Query Modification
		18.3.3 SPARQL Query Modification
		18.3.4 Query Modification for Enforcing Constraints
			18.3.4.1 SPARQL Query Filter
			18.3.4.2 Property Paths
			18.3.4.3 Property Path Queries
			18.3.4.4 Overview of Query Modification
			18.3.4.5 Graph transformation of a SPARQL Query BGP
			18.3.4.6 Match Pattern/ApplyPattern
			18.3.4.6.1 Processing Rules
			18.3.4.6.2 Enforcing Constraints by Graph Rewriting
	18.4 Our Approach to the Implementation of the Inference Controller
	18.5 Inference and Provenance
		18.5.1 Examples
		18.5.2 Approaches to the Inference Problem
			18.5.2.1 Domain Restriction
			18.5.2.2 Statistical Reasoning
			18.5.2.2.1 Machine Learning Techniques
		18.5.3 Inferences in Provenance
			18.5.3.1 Implicit Information in Provenance
		18.5.4 Use Cases of Provenance
			18.5.4.1 Dataset Documentation
			18.5.4.2 Pinpoint Errors in a Process
			18.5.4.3 Identifying Private Information in Query Logs
			18.5.4.4 Use Case: Who Said That?
			18.5.4.5 Use Case: Cheating Dictator
		18.5.5 Processing Rules
	18.6 Implementation Details
		18.6.1 Architecture
		18.6.2 Provenance in a Healthcare Domain
			18.6.2.1 Populating the Provenance Knowledge Base
			18.6.2.2 Generating and Populating the Knowledge Base
			18.6.2.3 Generating Workflows
			18.6.2.4 Properties of the Workflow
		18.6.3 Policy Management
			18.6.3.1 Policy Screen
			18.6.3.2 Parsing Process
			18.6.3.3 High-Level Policy Translation
			18.6.3.4 SWRL Rule Assembler
			18.6.3.5 A SWRL Policy Translation
			18.6.3.6 Description Logics Rule Assembler
			18.6.3.7 Cardinality Restrictions
			18.6.3.8 hasValue Restriction
			18.6.3.9 Supporting Restrictions
			18.6.3.9.1 A DL policy translation
			18.6.3.10 Access Control Policy Assembler
			18.6.3.11 An Access Control Policy Translation
			18.6.3.12 Redaction Policy Assembler
			18.6.3.13 A Redaction Policy Translation
		18.6.4 Explanation Service Layer
	18.7 Generators
		18.7.1 Selecting background information
		18.7.2 Background generator module
			18.7.2.1 Patient Generator
			18.7.2.2 Physician Generator
			18.7.2.3 Hospital Generator
		18.7.3 Miscellaneous generators
		18.7.4 Workflow generator
		18.7.5 Annotating the Workflow
		18.7.6 Generating workflows
		18.7.7 Incomplete information in the databases
	18.8 Use Case: Medical Example
		18.8.1 Semantic associations in the workflow
	18.9 Implementing Constraints
		18.9.1 Query modification for enforcing constraints
			18.9.1.1 Query Filter
	18.10 Next Generation Inference Controllers
	18.11 Summary and Directions
	References
19. Emerging Applications for Secure Data Science: Internet of Transportation Systems
	19.1 Introduction
	19.2 Integration of Cyber Security and AI
	19.3 Security and Privacy for the Internet of Transportation Systems
	19.4 AI and Security for Cloud-based Internet of Transportation Systems
	19.5 Summary and Directions
	References
Conclusion to Part IV
20. Summary and Directions
	20.1 About This Chapter
	20.2 Summary of this Book
	20.3 Directions for Secure Data Science
	20.4 Where Do We Go From Here?
Appendix. Data Management Systems - Developments and Trends
	A.1 Introduction
	A.2 Developments in Database Systems
	A.3 Status, Vision, and Issues
	A.4 Data Management Systems Framework
	A.5 Building Information Systems from the Framework
	A.6 Relationship Between the Texts
	A.7 Summary and Directions
	References
Index