Contents
A Survey of Machine Learning for Network Fault Management
1 Introduction
2 Network Fault Management
3 Pattern Mining-Based Approaches
3.1 Episode and Association Rules Mining-Based Approaches
3.2 Sequential Pattern Mining-Based Approaches
3.3 Clustering-Based Approaches
3.4 Summary and Perspective
4 Machine Learning-Based Approaches
4.1 Artificial Neural Networks-Based Approaches
4.2 Decision Tree-Based Approaches
4.3 Bayesian Networks-Based Approaches
4.4 Support-Vector Machine-Based Approaches
4.5 Dependency Graph-Based Approaches
4.6 Other Approaches
4.7 Summary and Perspective
5 Conclusion
References
Deep Bidirectional Gated Recurrent Unit for Botnet Detection in Smart Homes
1 Introduction
2 Deep BGRU Method for Botnet Detection in IoT Networks
2.1 Bidirectional Gated Recurrent Unit
2.2 The Proposed Method for Selection of Optimal BGRU Hyperparameters
2.3 Deep BGRU Classifier for IoT Botnet Detection
3 Results and Discussion
3.1 Influence of Activation Functions on Classification Performance
3.2 Influence of the Number of Epochs on Classification Performance
3.3 Influence of the Number of Hidden Layers on Classification Performance
3.4 Influence of Hidden Units on Classification Performance
3.5 Influence of Batch Size on Classification Performance
3.6 Influence of Optimizers on Classification Performance
3.7 Performance of Deep BGRU-Based Multi-class Classifier
4 Conclusion
References
Big Data Clustering Techniques: Recent Advances and Survey
1 Introduction
2 Clustering Techniques
2.1 Partitioning Methods
2.2 Hierarchical Clustering Methods
2.3 Density-Based Methods
2.4 Grid-Based Algorithms
2.5 Model-Based Methods
3 Big Data Clustering Approaches
3.1 Data Reduction-Based Methods
3.2 Centre-Based Reduction Methods
3.3 Parallel Techniques
4 Big Data Clustering Applications
4.1 Healthcare
4.2 Internet of Things (IoT)
4.3 Anomaly Detection
4.4 Social Media
5 Discussion
6 Challenges and Future Research Work
7 Conclusion
References
A Survey of Network Intrusion Detection Using Machine Learning Techniques
1 Introduction
2 Machine Learning
2.1 Supervised Learning
2.2 Un-Supervised Learning
2.3 Semi-supervised Learning
2.4 Reinforcement Learning
2.5 Ensemble Learning
2.6 Feature Selection
3 Machine Learning Based Intrusion Detection System
3.1 Intrusion Detection System (IDS)
4 Hybrid Intrusion Detection Systems
5 Evaluations of Intrusion Detection System
5.1 KDD Cup-â99 Dataset
5.2 NSL-KDD Dataset
5.3 Kyoto 2006 + Dataset
5.4 Performance Metrics
6 Research Opportunities
7 Conclusion
References
Indexing in Big Data Mining and Analytics
1 Introduction
1.1 Objective of the Chapter
1.2 Taxonomy of the Chapter
2 Index and Indexing
2.1 Index Architecture and Indexing Types
2.2 Bitmap Index
2.3 Dense Index
2.4 Sparse Index
3 Online Indexes
3.1 Online Indexing
3.2 Database Cracking
3.3 Adaptive Merge
3.4 Big Data Analytics Platforms
4 Inherent Indexes in MapReduce
4.1 Per Document Indexing
4.2 Per-Posting List Indexing
5 User-Defined Indexing in MapReduce
6 Conclusion
References
Two-Steps Wrapper-Based Feature Selection in Classification: A Comparison Between Continuous and Binary Variants of Cuckoo Optimisation Algorithm
1 Introduction
2 Background
2.1 Cuckoo Optimisation Algorithm
2.2 Binary Cuckoo Optimisation Algorithm
2.3 Related Works
3 Proposed Wrapper-Based Feature Selection Approaches
3.1 BCOA and COA for Feature Selection
3.2 A Combined Fitness Function for BCOA and COA Feature Selection
4 Experimental Design
4.1 Experimental Datasets
4.2 Experimental Parameter Settings
4.3 Benchmark Approaches
5 Results and Discussions
5.1 Results of the Proposed BCOA-FS and COA-FS
5.2 Results of the Proposed BCOA-2S and COA-2S
5.3 Comparison Between Proposed Methods and Classical Methods
5.4 Comparison Between Proposed Methods and Other Existing Methods
5.5 Comparisons Between BCOA and COA
5.6 Further Discussions
6 Conclusions and Future Work
References
Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier
1 Introduction
2 Related Works
3 Methodology
3.1 Method of Data Collection and Preparation
3.2 Feature Subset Selection
3.3 Meta-Heuristics Algorithms
3.4 Classification
3.5 Cross-Validation
3.6 Evaluation Metric
4 Results and Discussion
4.1 Modelling and Interpretation
4.2 Models Comparison Based on SVM as a Classifier
4.3 Models Comparison Based on RF as a Classifier
4.4 Comparison with Existing Models
5 Conclusion
References
Improved Cloud-Based N-Primes Model for Symmetric-Based Fully Homomorphic Encryption Using Residue Number System
1 Introduction
2 Related Works
3 Research Method
3.1 Homomorphic Encryption
3.2 N-Primes Model
3.3 Residue Number System
3.4 Proposed RNS-Based N-Primes Model for Symmetric FHE
4 Results and Discussion
5 Conclusion
Appendix A: Ciphertext
References
Big Data Analytics: Partitioned B+-Tree-Based Indexing in MapReduce
1 Introduction
1.1 Objective of the Chapter
2 Literature Review
2.1 Inverted Index in MapReduce
2.2 User-Defined Indexing in MapReduce
3 Methodology
3.1 Partitioned B+-Tree
3.2 InputSplit as Component of Choice in the HDFS
4 Experimental Results and Discussion
4.1 The Dataset
4.2 Index Building Using the Datasets
4.3 Test Queries
4.4 The Experiment and Its Setup
4.5 Index Creation Performance Evaluation
5 Results and Discussions
6 Conclusion
References
Internet of Vehicle for Two-Vehicle Look-Ahead Convoy System Using State Feedback Control
1 Introduction
2 System Architecture
2.1 IoV Components
2.2 IoV Architecture for Two-Vehicle Look-Ahead Convoy
2.3 Platform Used for Implementation of the Model
3 Modeling of the Two Look-Ahead Vehicle Convoy Strategy
4 Vehicle Dynamic
4.1 SFC Design Using Pole-Placement Approach
4.2 Design Procedure of the SFC via Pole Placement Technique
4.3 Controllability System Test
5 Result and Discussion
6 Conclusion and Future Work
References
Vehicle Following Control with Improved Information Flow Using Two-Vehicle-Look-Ahead-Plus-Rear-Vehicle Topology
1 Introduction
2 Single-Vehicle External Dynamics
2.1 Aerodynamic Drag
2.2 Viscous Friction Drag
2.3 Rolling Resistance Force
2.4 Simplified Vehicle Dynamics
3 Following Vehicle Convoy Dynamics
4 Turning of Gains and Simulation
5 Results and Discussion
5.1 Comparison of the One-Vehicle Look-Ahead and Two-Vehicle Look-Ahead Against the Proposed Topology
6 Conclusion and Further Work
References
Extended Risk-Based Context-Aware Model for Dynamic Access Control in Bring Your Own Device Strategy
1 Introduction
2 Background
2.1 Dynamic Access Control in BYOD Strategy
2.2 Bayesian Network
3 Related Work
3.1 Risk Evaluation Model
3.2 Context-Aware Access Control Model
3.3 Finding from Related Work
4 Proposed ExtSRAM Model
4.1 Contextual Risk Factors
4.2 Enterprise Environment
5 ExtSRAM Process Flow
5.1 Assumptions on ExtSRAM Model
5.2 ExtSRAM Methodology
6 Theoretical Validation of the Model
6.1 Soundness of ExtSRAM
6.2 Completeness of ExtSRAM
7 Future Research Directions
8 Conclusion
References