Emerging Trends in Cybersecurity Applications

✍ Scribed by Kevin Daimi, Abeer Alsadoon, Cathryn Peoples, Nour El Madhoun

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 464
• Category: Library

✦ Synopsis

This book provides an essential compilation of relevant and cutting edge academic and industry work on key cybersecurity applications topics. Further, it introduces cybersecurity applications to the public at large to develop their cybersecurity applications knowledge and awareness. The book concentrates on a wide range of advances related to Cybersecurity Applications which include, among others, applications in the areas of Data Science, Internet of Things, Artificial Intelligence, Robotics, Web, High-Tech Systems, Cyber-Physical Systems, Mobile Devices, Digital Media, and Cloud Computing. It introduces the concepts, techniques, methods, approaches and trends needed by cybersecurity application specialists and educators for keeping current their cybersecurity applications knowledge. Further, it provides a glimpse of future directions where cybersecurity applications are headed. The book can be a valuable resource to applied cybersecurity experts towards their professional development efforts and to students as a supplement to their cybersecurity courses.

✦ Table of Contents

Preface
Acknowledgments
Contents
About the Editors
Part I Internet of Things Applications Security
Ephemeral Elliptic Curve Diffie-Hellman to Secure Data Exchange in Internet of Medical Things
1 Introduction
2 Related Work
3 Proposed Approach
4 Experimental Results
5 Conclusion
References
End-to-End Security for IoT Communications: A Practical Implementation
1 Introduction
2 Security Issues in IoT Devices
3 Cloud-Based IoT Architecture
4 The Plug-Pair-Play (P3) Model to Establish a Secure Communication Channel
4.1 Secure Communication Between User and Gateway
4.2 Secure Communication Between Device and Gateway
4.3 Setting Up Shared Key for Owner
4.4 Setting Up Shared Key for Delegate
4.5 Secure Communication Between User and Device
5 Using P3 Connection Model to Update Device Firmware
6 Model Evaluation
6.1 Data Security
6.2 Memory Utilization
7 Conclusion
References
A Novel Transfer Learning Model for Intrusion Detection Systems in IoT Networks
1 Introduction
2 Background
2.1 Transfer Learning
2.2 Maximum Mean Discrepancy (M2D)
2.3 AutoEncoder
3 Related Work
4 Proposed Deep Transfer Learning Model
4.1 System Structure
4.2 Multi-Maximum Mean Discrepancy AutoEncoder
4.3 Training and Predicting Process Using M2DA
4.3.1 Training Process
4.3.2 Predicting Process
5 Experiment Description
5.1 Bot-IoT Datasets (IoT Datasets)
5.2 Evaluation Metric
5.3 Experimental Setting
5.3.1 Hyper-Parameter Setting
5.3.2 Experimental Set
6 Result and Discussion
6.1 Effectiveness of Transferring Task
6.2 Accuracy Comparison
6.3 Complexity
7 Conclusion
References
Part II Internet, Network and Cloud Applications Security
An Approach to Guide Users Towards Less Revealing InternetBrowsers
1 Introduction
2 Preliminaries
2.1 HTTP
2.2 User-Agent Request Header
2.3 CVE
2.4 NVD
2.5 CVSS
3 Methodology
3.1 The Relative Score
3.2 User Study
3.3 Survey Design
3.4 Results
3.5 Discussion
3.6 CVSS Score
3.7 Final Exposure Score
4 Data Set
4.1 Summary of the Data Set
5 Implementation
6 Related Works
7 Conclusion and Future Work
Appendix
A Survey Questions
A.1 Demografic Questions
A.2 Question Set 1
A.3 Question Set 2
A.4 Question Set 3
A.5 Question Set 4
References
Analysing the Threat Landscape Inside the Dark Web
1 Introduction
2 Literature Review
2.1 The Deep Web
2.2 The Dark Web
2.3 Attacks on TOR
2.3.1 Eclipse Attacks Against TOR Hidden Services
2.3.2 Website Fingerprinting Attack
2.3.3 RAPTOR (Routing Attacks on Privacy in TOR)
2.4 The Dark Web Activity Detection Methods
2.4.1 MLP (Machine Learning Perceptron)
2.4.2 Hadoop-Based Dark Web Threat Intelligence Analysis Framework
2.4.3 Black Widow
2.4.4 Vector Space Model
2.4.5 Dark Web Forum Visual Analysis Platform
2.4.6 The Methodology of Dark Web Monitoring
3 Summary of the Findings
4 Gap Analysis of the Existing Techniques
5 Research Design and Methodological Approach
6 Conclusion
References
A Secured 5G Network Slices Auction Broker
1 Introduction
2 Brokerage Model
2.1 The Business Model of the Network Slice Broker
2.2 Broker's Business Plan Executive Summary
2.3 Use of Software-Defined Networking
3 Blockchain for Registering Slice Deals
4 Dispute for Resources
4.1 Simulations
5 Conclusions
References
Applying Zero Trust Architecture and Probability-Based Authentication to Preserve Security and Privacy of Data in the Cloud
1 Introduction
2 Cloud Computing: Rethinking Security
2.1 Traditional Network Structure
2.2 Cloud Network Structure
2.2.1 Shared Responsibilities
2.2.2 Cloud Deployment
2.2.3 Cloud Geography
2.3 Edge and Fog Computing
2.4 Cloud Threats
3 Data Privacy in the Cloud
3.1 Data Security vs. Data Privacy
3.2 Data Privacy Complexity
3.3 Geopolitical Issues
4 Zero Trust Architecture in the Cloud
4.1 Secure Protocols
4.1.1 Do Not Trust the Intranet
4.1.2 Securing Networks
4.2 Legacy Applications
4.3 Data Classification and Categorisation
4.3.1 Data Classification
4.3.2 Data Categorisation
4.4 Data Encryption
4.4.1 Data at Rest
4.4.2 Data in Transit
4.4.3 Key Management
4.4.4 Data Masking
4.5 Access Control
4.5.1 Role-Based Access Control
4.5.2 Attribute-Based Access Control
4.5.3 Mandatory Access Control
4.5.4 Discretionary Access Control
4.6 Email Security
4.6.1 Secure Email Protocols
4.6.2 User Education
4.7 Protecting Edge Devices
5 Probability-Based Authentication
5.1 Applying Authentication Everywhere
5.2 Logging
5.3 User Identity or Entity
5.4 Multi-Factor Authentication
5.5 Network Access
5.6 Device Health
5.7 Managing Authentication Failure
6 Zero Trust Data
6.1 Zero Trust Data Implementation
7 Conclusions
References
DataCookie: Sorting Cookies Using Data Mining for Prevention of Cross-Site Scripting (XSS)
1 Introduction
2 DataCookie Model
2.1 System Setup
2.2 Processing and Cleaning
2.3 Data Analysis
2.4 Modeling
2.5 Performance Evaluation
2.6 Deployment
3 Analysis of Results
3.1 Security Analysis
3.2 Efficiency Analysis
4 Conclusions and Future Work
References
Part III Vehicle Applications Security
Analysing Cyber Attacks and Risks in V2X-Assisted Autonomous Highway Merging
1 Introduction
2 Related Work
3 Reference Architecture for Autonomous Highway Merging
4 Abuse Cases
4.1 Attack Surface Analysis
4.2 Tampering with the Input to the Image Object Detector
4.3 Jamming Infrastructure Radar
5 Threat Modelling Methodology
5.1 Cyber Security Requirements
5.2 TARA+
6 Risk Assessment and Mitigation Schemes
7 Comparative Study
8 Conclusion
References
A Machine Learning Framework for Intrusion Detection in VANET Communications
1 Introduction
2 Security of VANET Communications
2.1 Security Attacks and Vulnerabilities in VANET
2.2 Security Countermeasures
2.3 ML-Based Intrusion Detection Systems for VANETs
3 Proposed ML Framework
3.1 First Phase: Dataset Description
3.2 Second Phase: Data Preprocessing
3.3 Third Phase: Standalone and Ensemble Learning Techniques
4 Experimental Results
4.1 Performance Metrics
4.2 Evaluation of ML Models Before and After SMOTE
4.3 Feature Selection and Analysis
5 Conclusion
References
Part IV Mobile Applications Security
The Implementation of Uncertainty Models for Fraud Detection on Mobile Advertising
1 The Competition Between Fraud and Anti-fraud on Mobile Advertisements
2 Analysis Fraud and Risk with Fuzzy and Rough Sets on Mobile Advertising Fraud Detection
2.1 The Implementation of Fuzzy Set Theory to Mobile Fraud Detection
2.1.1 Fuzzy Set and Uncertainty Define
2.1.2 Fuzzy Statistics on Anti-fraud Methods
2.1.3 Anti-fraud Data Analyzing Processes
2.1.4 Testing Log Generator and Test Result
2.2 Implement of Rough Set Theory on Mobile Fraud Detection
2.2.1 Rough Set Theory and Dependency
2.2.2 Calculate the Dependency Metric
2.2.3 Test Result
3 Potential Impact on Future Applications for Online Media Anti-fraud Detection
3.1 Reduce or Endow Weight to the Dimensions of Parameters for Machine Learning
3.2 Data Analysis Support for Advertisers
3.3 Membership Degree for Fuzzy Tagging
4 Conclusion and Future Work
References
Improving Android Application Quality Through Extendable, Automated Security Testing
1 Introduction
2 Related Work
3 Android Application Vulnerability Analysis and Feedback System
4 Validating and Testing the System
5 Conclusions and Future Work
References
Part V Energy Applications Security
A Provably Secure Data Sharing Scheme for Smart Gas Grid in Fog Computing Environment
1 Introduction
1.1 Smart Gas Networks
1.2 Smart Gas Meters
1.3 Chapter Contribution
1.4 Chapter Organization
2 Related Work
3 Complexity Assumptions
3.1 Formal Model of Proposed IBPSC-SGG-FCE Scheme
3.2 Security Definition
4 Proposed IBPSC-SGG-FCE Scheme
4.1 Construction of IBPSC-SGG-FCE
5 Security Analysis of the Proposed Scheme
5.1 Performance Analysis
5.2 Computational Cost Analysis
5.3 Communication Cost Analysis
6 Conclusion
References
Countering Cybersecurity Threats in Smart Grid Systems Using Machine Learning
1 Introduction
2 Smart Grid Background and Technology
2.1 The Need for Smart Grid
2.2 The NIST Smart Grid Conceptual Model and Components
2.3 Smart Grid System's Technology
3 The Cybersecurity Aspects of the Smart Grid
3.1 History of Attacks on Smart Grid Systems
3.2 Cyberattack Detection and Mitigation Techniques
4 Security Risks in Smart Grid
4.1 Security Requirements for Smart Grid Systems
4.2 Security Risks, Threats, and Vulnerability Concerns
5 Machine Learning and Types of Machine Learning
5.1 Data Analysis in Machine Learning
5.2 Supervised Learning
5.3 Unsupervised Learning
5.4 Reinforcement Learning
5.5 Semi-supervised Learning
6 Applications of Various Machine Learning Techniques in Smart Grids
7 Conclusions
References
Preserving the Privacy and Cybersecurity of Home Energy Data
1 Introduction
2 Background and Related Work
3 Location Inference Attack
3.1 Threat Model
3.2 Our Inverter Dataset Across Two Countries
3.3 ERA5-Land Reanalysis
3.4 PV Simulation
4 Evaluation
5 Mitigation
5.1 Overview of Privacy-Preserving Sharing Techniques
5.1.1 Differential Privacy
5.1.2 Federated Learning
5.1.3 Homomorphic Encryption
5.2 Applying Privacy-Preserving Sharing Techniques to Use Cases of Solar Energy Data
5.2.1 Solar Generation with Scheduling
5.2.2 Solar Recommendation System
6 Case Study—Billing with Homomorphic Encryption
7 Concluding Remarks
References
Part VI Cyber-Physical Systems, Artificial Intelligence, and Software Applications Security
Non-stationary Watermark-Based Attack Detection to Protect Cyber-Physical Control Systems
1 Introduction
1.1 Objectives and Contributions
2 Dynamic Challenge-Response Authentication Scheme
2.1 Problem Formulation
2.2 Detector Based on a Stationary Signature
2.3 Cyber-Physical Adversaries
2.4 Multi-Signature Based Detector
2.4.1 Validation Against Non-parametric Cyber-Physical Adversaries
2.4.2 Validation Against Parametric Cyber-Physical Adversaries
2.5 Discussion
3 Adaptive Detection Based on Control Theory
3.1 Parametric Cyber-Physical Adversaries Detection
3.2 Numerical Use Case
4 Experimental Testbed for the Detection of Cyber-Physical Attacks
4.1 Architecture Design
4.2 Adversary Implementation
4.3 Attacks and Anomalies Detection
4.4 Experimental Results
5 Future Directions and Research Trends
6 Conclusion
References
Cybersecurity Applications in Software: Data-Driven Software Vulnerability Assessment and Management
1 Software Vulnerability Assessment and Management
1.1 Vulnerability and Vulnerability Disclosure
1.2 Exploit and Exploitability
1.3 Lifecycle of a Vulnerability
1.4 Cybersecurity Ecosystem
2 Mainstream Vulnerability and Exploit Databases
2.1 CVE: Common Vulnerabilities and Exposures database
2.2 NVD: National Vulnerability Database
2.3 CVE Details
2.4 EDB: Exploit Database
3 Common Vulnerability Scoring System
3.1 CVSS Metric Groups
3.2 CVSS Scores
3.3 Limitations of CVSS
4 Vulnerability Exploitability Prediction and Analysis
4.1 Vulnerability Exploitability Prediction
4.2 Online Vulnerability Exploitability Prediction
4.3 Vulnerability Exploitation Time Prediction
5 Summary
References
Application of Homomorphic Encryption in Machine Learning
1 Introduction to Homomorphic Encryption
1.1 HE Schemes
1.2 HE Libraries
1.3 FHE Restrictions
2 Privacy-Preserving in Machine Learning (PPML): HE Solutions
2.1 Logistic Regression
2.2 Naive Bayes and Decision Trees
2.3 K-Nearest Neighbors
2.4 Neural Networks and Deep Learning
2.4.1 Privacy-Preserving Deep Learning: Private Training
2.4.2 Privacy-Preserving Deep Learning: Private Inference
2.5 Clustering
2.5.1 Collaborative Clustering
2.5.2 Individual Clustering
3 Discussion and Challenges
References
Part VII Other Security Applications
The Design of Ethical Service-Level Agreements to Protect Cyber Attackers and Attackees
1 Introduction
2 The Challenges of Managing Online Crime
3 SLA Brokering
4 Proposed SLA Brokering Solution
5 SLA Assignments
6 Case Studies
7 Conclusions and Further Work
References
Defense Against Adversarial Attack on Knowledge Graph Embedding
1 Introduction
2 Related Works
2.1 Knowledge Graph Embedding
2.2 Adversarial Attack Against Knowledge Graph Embedding
2.3 Adversarial Defense Against Adversarial Attack
3 Two-Stage Adversarial Defense Approaches
3.1 Adversarial Training
3.2 Perturbation Detection
4 Experiment Results
4.1 Datasets
4.2 Baseline and Target Models
4.3 Metrics and Experiment Settings
4.4 Results and Analysis
4.5 Case Study
5 Conclusions
References
Index