System Reliability and Security: Techniques and Methodologies

✍ Scribed by Javaid Iqbal, Faheem Syeed Masoodi, Ishfaq Ahmad Malik, Shozab Khurshid, Iqra Saraf, Alwi M. Bamhdi

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 273
• Category: Library

✦ Synopsis

ecause of the growing reliance on software, concerns are growing as to how reliable a system is before it is commissioned for use, how high the level of reliability is in the system, and how many vulnerabilities exist in the system before its operationalization. Equally pressing issues include how to secure the system from internal and external security threats that may exist in the face of resident vulnerabilities. These two problems are considered increasingly important because they necessitate the development of tools and techniques capable of analyzing dependability and security aspects of a system. These concerns become more pronounced in the cases of safety-critical and mission-critical systems.

System Reliability and Security: Techniques and Methodologies focuses on the use of soft computing techniques and analytical techniques in the modeling and analysis of dependable and secure systems. It examines systems and applications having complex distributed or networked architectures in such fields as

■ Nuclear energy

■ Ground transportation systems

■ Air traffic control

■ Healthcare and medicine

■ Communications

System reliability engineering is a multidisciplinary field that uses computational methods for estimating or predicting the reliability aspects of a system and analyzing failure data obtained from real-world projects. System security is a related field that ensures that even a reliable system is secure against accidental or deliberate intrusions and is free of vulnerabilities. This book covers tools and techniques, cutting-edge research topics, and methodologies in the areas of system reliability and security. It examines prediction models and methods as well as how to secure a system as it is being developed.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
List of Contributors
1 GNN Approach for Software Reliability
Acronyms With Definitions
1.1 Introduction
1.2 Software Defect Prediction Approaches
1.2.1 Traditional SDP Techniques
1.2.2 Deep Learning in SDP
1.2.3 Summary
1.3 Understanding the Structure of a Software Program as Graph
1.3.1 Abstract Syntax Tree
1.3.2 Function Call Graph
1.3.3 Data Flow Graph
1.4 GNN Approach for Defect Rate Prediction Using Graph Structure of a Program
1.4.1 GNN Architecture
1.4.1.1 Input Layer
1.4.1.2 GNN Layers
1.4.1.3 Output Layer
1.4.2 Applying GNN to AST
1.5 Conclusion
References
2 Software Reliability Prediction Using Neural Networks: A Non-Parametric Approach
2.1 Introduction
2.2 Approaches for Software Reliability Modeling
2.2.1 Parametric Software Reliability Growth Models
2.2.1.1 Yamada Delayed S-Shaped Model
2.2.1.2 Goel–Okumoto Model
2.2.1.3 Generalized Goel NHPP Mode
2.2.1.4 Logistic Growth Curve Model
2.2.1.5 MO Model
2.2.1.6 Pham–Nordmann–Zhang (PNZ) Model
2.2.1.7 Pham–Zhang (P-Z) Model
2.2.1.8 Yamada Imperfect Debugging Model 1
2.2.1.9 Yamada Imperfect Debugging Model 2
2.2.2 Non-Parametric Reliability Growth Models
2.3 Software Reliability
2.3.1 Software Reliability Measures
2.3.2 Parameter Estimation Techniques
2.3.3 Failure Data Sets
2.4 ANN Approach for Reliability
2.5 Conclusion
References
3 Analysis and Modeling of Software Reliability Using Deep Learning Methods
3.1 Introduction
3.2 Related Work
3.2.1 Novel Deep Learning Solutions for Software Defect Detection and Reliability
3.2.2 Transformers as a Novel Proposed Solution
3.2.2.1 Introduction to Word Embedding and Word2vec
3.2.2.2 Transformer Deep Learning Model
3.3 Conclusion
References
4 Fixed-Design Local Polynomial Approach for Estimation and Hypothesis Testing of Reliability Measures
4.1 Introduction
4.2 Popular Component Reliability Measures
4.2.1 Empirical Estimators
4.2.2 Fixed-Design Local Polynomial Estimators
4.2.2.1 Fixed-Design Local Polynomial Estimators: Asymptotic Properties
4.2.2.2 Dealing With a Randomly Censored Dataset
4.2.2.3 Optimal Bin Width and Bandwidth Selection
4.2.3 Performance of Proposed Estimators
4.3 Non-Parametric Hypothesis Tests for Comparing Reliability Functions
4.3.1 Statistical Comparison of Expected Inactivity Time Functions of Two Populations
4.3.1.1 Critical Values of the Test Statistics
4.3.2 Statistical Comparison of Mean Residual Life Functions of Two Populations
4.3.2.1 Critical Values of the Test Statistics
4.3.2.2 Using Bootstrapping to Calculate the Critical Value
4.3.3 Evaluating Efficiency of the Proposed Hypothesis Tests
4.3.4 Practical Performance
4.4 Conclusion
References
5 Reliability Analysis of Relation Between Urbanization, Vegetation Health, and Heat Island Through Markov Chain Model
5.1 Introduction
5.2 Materials and Methods
5.2.1 Normalized Difference Vegetation Index (NDVI)
5.2.2 Normalized Difference Built-Up Index (NDBI)
5.2.3 Land Surface Temperature Method
5.2.4 Analytical Hierarchy Process (AHP)
5.2.5 Markov Chain Model
5.2.5.1 Governmental Model
5.2.6 Finding Supportive Policy
5.3 Result and Discussion
5.3.1 Temporal Analysis of Land Use and Land Cover of Kolkata Municipal Area
5.3.2 Temporal Analysis of Normalized Difference Vegetation Index (NDVI) of Kolkata Municipal Area
5.3.3 Temporal Analysis of Normalized Difference Built-Up Index (NDBI) of Kolkata Municipal Area
5.3.4 Scenario of Urban Heat Island of Kolkata Municipal Area From 1999 to 2022
5.3.5 Analytical Hierarchy Process
5.3.6 Markov Chain
5.4 Conclusion
References
6 Modeling and IoT (Internet of Things) Analysis for Smart Precision Agriculture
6.1 Introduction
6.1.1 How IoT in Agriculture Has Left Its Mark
6.1.2 Application of IoT in Agriculture
6.1.3 Environmental Factors
6.1.4 Precision Farming
6.1.5 Smart Greenhouses
6.1.6 Data Analytics
6.1.7 Agricultural Drones
6.2 Related Work
6.2.1 User-Centered Design Models
6.2.2 Internet of Things: Protocols and Architectures
6.2.3 Internet of Things Technologies Applied On PA Scenarios
6.2.4 Edge and Fog Computing Paradigms: Evolution of the Internet of Things, Cloud, and Machine Learning
6.2.5 Automated Greenhouse Technologies
6.3 Materials and Methods
6.3.1 User-Centered Analysis and Design
6.3.2 Data Analysis: Configuration of Edge and Fog Computing
6.3.3 Things and Communication
6.3.4 Network Platform: Development and Design
6.3.5 Platform Development
6.4 Conclusions and Future Work
References
7 Engineering Challenges in the Development of Artificial Intelligence and Machine Learning Software Systems
7.1 Introduction
7.2 Categories of Challenges in AI/ML Software Systems
7.2.1 Software Testing and Quality Assurance
7.2.2 Model Development
7.2.3 Project Management and Infrastructure
7.2.4 Requirement Engineering
7.2.5 Architecture Design and Integration
7.2.6 Model Deployment
7.2.7 Engineering
7.3 Summary
References
8 Study and Analysis of Testing Effort Functions for Software Reliability Modeling
8.1 Introduction
8.2 Summary of Some Famous TEFs Used in the Literature
8.3 Numerical Analysis of 12 TEFs Employed in This Study
8.4 Numerical Analysis
8.5 Conclusion
Acknowledgment
References
9 Summary of NHPP-Based Software Reliability Modeling With Lindley-Type Distributions
9.1 Introduction
9.2 NHPP-Based Software Reliability Modeling
9.2.1 Finite-Failure (Finite) NHPP-Based SRMs
9.2.2 Infinite-Failure (Infinite) NHPP-Based SRMs
9.3 Lindley-Type Distribution
9.4 Maximum Likelihood Estimation
9.5 Performance Illustration
9.5.1 Goodness-Of-Fit Performance
9.5.2 Predictive Performance
9.5.3 Software Reliability Assessment
9.6 Conclusion
References
10 Artificial Intelligence and Machine Learning Problems and Challenges in Software Testing
10.1 Introduction
10.1.1 Overview of Machine Learning and Artificial Intelligence
10.1.2 Impact of AI On Software Testing
10.1.3 Role of AI in Software Testing
10.2 Issues and Challenges of AI
10.2.1 Recognizing Test Data
10.2.2 Algorithmic Uncertainty
10.2.3 Measures of Effectiveness That Are Not Accurate
10.2.4 Data Splitting Into Training and Testing Sets
10.3 Related Work
10.3.1 Artificial Intelligence Overview
10.3.2 Artificial Neural Network
10.3.3 AI Planning
10.3.4 Machine Learning
10.3.5 Natural Language Processing (NLP)
10.3.6 Fuzzy Logic
10.4 Artificial Intelligence in Agriculture
10.4.1 Software Testing in the Area of Artificial Intelligence for Agriculture
10.4.2 Development Spurred By the Internet of Things (IoT)
10.5 Software Testing Overview
10.6 Tools
10.6.1 Testim.io
10.6.2 Appvance
10.6.3 Test.ai
10.6.4 Functioned
10.7 Conclusion
10.8 Future Work
References
11 Software Quality Prediction By CatBoost Feed-Forward Neural Network in Software Engineering
11.1 Introduction
11.2 Literature Review
11.2.1 Parameters That Influence Software Quality
11.2.1.1 Software Efficiency
11.2.1.2 Mode of Software Development
11.2.1.3 Developer Or Developer Team
11.2.2 Machine Learning Framework
11.2.3 Analysis With Respect to Existing Work
11.3 Methodology Or Framework
11.3.1 Exploratory Analysis
11.3.2 Data Preprocessing
11.3.3 Feature Engineering
11.3.4 Training and Testing the Model
11.3.5 Evaluation
11.4 Results
11.5 Conclusion
References
12 Software Security
12.1 Introduction
12.1.1 Software Security Process
12.2 Challenges and Requirements
12.2.1 Security Requirements Modeling
12.2.2 Validation Requirements Modeling
12.3 Software Security Vulnerabilities
12.3.1 Security Automation in Software-Defined Networks
12.3.2 Security Threat-Oriented Requirements Engineering Methodology
12.4 Environment and System Security
12.4.1 Levels of Security
12.4.2 Level I—Minimal Protection
12.4.3 Level II—Advanced Protection
12.4.4 Level III—Maximal Protection
12.5 Cloud Security
12.5.1 Infrastructure-As-A-Service (IaaS) and Platform-As-A-Service (PaaS)
12.5.2 Software-As-A-Service (SaaS)
12.5.3 Software Testing Metrics
12.6 Conclusion
References
13 Definitive Guide to Software Security Metrics
13.1 Introduction
13.2 Related Work
13.3 Software Security Measurement Primer
13.4 Security Metrics Taxonomies
13.5 Conclusion
References
14 Real-Time Supervisory Control and Data Acquisition (SCADA) Model for Resourceful Distribution and Use of Public Water
14.1 Introduction
14.2 Stage 1 (Automatic Water Pumping)
14.3 Stage 2 (Automatic Water Distribution in the City)
14.4 Stage 3 (Automatic Water Leakage Detection System)
14.5 Stage 4 (Pressure Or Storage Tank)
14.6 System Simulation Environment
14.7 Programmable Logic Controllers (PLCs)
14.8 Field Instruments
14.9 SCADA Software
14.10 InTouch Application Manager
14.11 Human–Machine Interface (HMI)
14.12 Research Tools and Techniques
14.12.1 A. Automatic Pumping of Water From Well
14.13 Automatic Water Pumping
14.13.1 B. Automatic Water Distribution System in the City
14.14 Automatic Water Distribution
14.14.1 C. Automatic Water Leakage Detection System
14.15 Water Leakage System
14.15.1 D. Automatic Water Pumping System Using SCADA
14.16 Automatic Water Pumping System
14.17 Storage Water Pumping
14.18 Conclusion
References
Index