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[null,"1.","Record Nr.",null,"UNISA996464436503316"]
[null,null,"Titolo",null,"Machine learning and data mining for emerging trend in cyber dynamics : theories and applications / / edited by Haruna Chiroma, 3 others"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-66288-8"]
[null,null,"Descrizione fisica",null,"1 online resource (315 pages) : illustrations"]
[null,null,"Disciplina",null,"354.81150006"]
[null,null,"Soggetti",null,"Industries"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Intro -- 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 "]
[{"$nulls":4},"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."]
[{"$nulls":4},"3.2 Centre-Based Reduction Methods -- 3.3 Parallel Techniques -- 4 Big Data Clustering Applications -- 4.1 Healthcare -- 4.2 Internet of Things (IoT) -- 4.3 Anomaly Detection -- 4.4 Social Media -- 5 Discussion -- 6 Challenges and Future Research Work -- 7 Conclusion -- References -- A Survey of Network Intrusion Detection Using Machine Learning Techniques -- 1 Introduction -- 2 Machine Learning -- 2.1 Supervised Learning -- 2.2 Un-Supervised Learning -- 2.3 Semi-supervised Learning -- 2.4 Reinforcement Learning -- 2.5 Ensemble Learning -- 2.6 Feature Selection -- 3 Machine Learning Based Intrusion Detection System -- 3.1 Intrusion Detection System (IDS) -- 4 Hybrid Intrusion Detection Systems -- 5 Evaluations of Intrusion Detection System -- 5.1 KDD Cup-'99 Dataset -- 5.2 NSL-KDD Dataset -- 5.3 Kyoto 2006 + Dataset -- 5.4 Performance Metrics -- 6 Research Opportunities -- 7 Conclusion -- References -- Indexing in Big Data Mining and Analytics -- 1 Introduction -- 1.1 Objective of the Chapter -- 1.2 Taxonomy of the Chapter -- 2 Index and Indexing -- 2.1 Index Architecture and Indexing Types -- 2.2 Bitmap Index -- 2.3 Dense Index -- 2.4 Sparse Index -- 3 Online Indexes -- 3.1 Online Indexing -- 3.2 Database Cracking -- 3.3 Adaptive Merge -- 3.4 Big Data Analytics Platforms -- 4 Inherent Indexes in MapReduce -- 4.1 Per Document Indexing -- 4.2 Per-Posting List Indexing -- 5 User-Defined Indexing in MapReduce -- 6 Conclusion -- References -- Two-Steps Wrapper-Based Feature Selection in Classification: A Comparison Between Continuous and Binary Variants of Cuckoo Optimisation Algorithm -- 1 Introduction -- 2 Background -- 2.1 Cuckoo Optimisation Algorithm -- 2.2 Binary Cuckoo Optimisation Algorithm -- 2.3 Related Works -- 3 Proposed Wrapper-Based Feature Selection Approaches -- 3.1 BCOA and COA for Feature Selection."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"in MapReduce -- 1 Introduction -- 1.1 Objective of the Chapter -- 2 Literature Review -- 2.1 Inverted Index in MapReduce -- 2.2 User-Defined Indexing in MapReduce -- 3 Methodology -- 3.1 Partitioned B+-Tree -- 3.2 InputSplit as Component of Choice in the HDFS."]
[{"$nulls":4},"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."]
[{"$nulls":4},"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."]