Computational Intelligence for Cybersecurity Management and Applications:
The book offers comprehensive coverage of the essential topics, including machine Learning and Deep Learning for cybersecurity, blockchain for cybersecurity and privacy, security engineering for Cyber-physical systems, AI and Data Analytics techniques for cybersecurity in smart systems, trust in dig...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Milton
Taylor & Francis Group
2023
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| Schriftenreihe: | Advances in Cybersecurity Management Series
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| Schlagworte: | |
| Online-Zugang: | DE-573 Volltext |
| Zusammenfassung: | The book offers comprehensive coverage of the essential topics, including machine Learning and Deep Learning for cybersecurity, blockchain for cybersecurity and privacy, security engineering for Cyber-physical systems, AI and Data Analytics techniques for cybersecurity in smart systems, trust in digital systems |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (xii, 235 Seiten) |
| ISBN: | 9781000853346 9781003319917 |
| DOI: | 10.1201/9781003319917 |
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| 100 | 1 | |a Maleh, Yassine |d 1987- |e Verfasser |0 (DE-588)1190215594 |4 aut | |
| 245 | 1 | 0 | |a Computational Intelligence for Cybersecurity Management and Applications |
| 264 | 1 | |a Milton |b Taylor & Francis Group |c 2023 | |
| 264 | 4 | |c ©2023 | |
| 300 | |a 1 Online-Ressource (xii, 235 Seiten) | ||
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| 490 | 0 | |a Advances in Cybersecurity Management Series | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Editor biographies -- Contributors -- Section I: Big Data and Computational Intelligence for Cybersecurity Management and Applications -- 1. Big Data and Blockchain for Cybersecurity Applications: Challenges and Solutions -- 1.1 Introduction -- 1.2 Benefits of Big Data Analytics for Manufacturing Internet of Things -- 1.2.1 Improving Factory Operations and Production -- 1.2.1.1 Reducing Machine Downtime -- 1.2.1.2 Improving Product Quality -- 1.2.1.3 Enhancing Supply Chain Efficiency -- 1.2.1.4 Monitoring Manufacturing Process -- 1.2.1.5 Reduction in Energy Consumption and Energy Costs -- 1.2.1.6 Reduction of Scrap Rate -- 1.3 Big Data Analytics Research in IoT: Issues and Challenges -- 1.3.1 Data Acquisition Challenges -- 1.3.2 Data Preprocessing and Storage -- 1.3.2.1 Data Integration -- 1.3.2.2 Redundancy Reduction -- 1.3.2.3 Data Cleaning and Data Compression -- 1.3.2.4 Reliability and Persistence of Data Storage -- 1.3.2.5 Scalability -- 1.3.2.6 Efficiency -- 1.3.3 Data Analytics -- 1.3.3.1 Data Temporal and Spatial Correlation -- 1.3.3.2 Efficient Data Mining Schemes -- 1.3.3.3 Privacy and Security -- 1.3.4 Uncertainty Challenges and Computational Intelligence Techniques -- 1.3.4.1 Volume -- 1.3.4.2 Variety -- 1.3.4.3 Velocity -- 1.3.4.4 Veracity -- 1.3.4.5 Value -- 1.4 Computational Intelligence Techniques -- 1.4.1 Fuzzy Logic -- 1.4.2 Evolutionary Algorithms -- 1.4.3 Artificial Neural Networks -- 1.5 Integration of Big Data with Business Intelligence -- 1.5.1 Bikers Haven Restaurant Case Study -- 1.5.1.1 Problem -- 1.5.1.2 Solution -- 1.5.1.3 Methodology -- 1.5.1.4 Results -- 1.5.2 ChangQing Drilling Company Case Study -- 1.5.2.1 Data Integration -- 1.5.2.2 Implementation of Business Intelligence -- 1.5.2.3 Discussion | |
| 505 | 8 | |a 1.6 Bitcoin Adoption and Rejection -- 1.6.1 Bitcoin Adoption -- 1.6.2 Bitcoin Rejection -- 1.6.2.1 Bangladesh -- 1.6.2.2 Bolivia -- 1.6.2.3 Russia -- 1.6.2.4 Vietnam -- 1.6.3 Advantages and Disadvantages -- 1.6.3.1 Advantages -- 1.6.3.1.1 Personal Data Protection -- 1.6.3.1.2 Lower Transaction Fee -- 1.6.3.1.3 Protection through Speed of Transfer -- 1.6.3.1.4 Immunity to Inflation -- 1.6.3.2 Disadvantages -- 1.6.3.2.1 Lack of Solid Anonymity -- 1.6.3.2.2 Prone to Scams -- 1.6.3.2.3 Trust -- 1.7 Blockchain in Cybersecurity -- 1.7.1 Improving Cybersecurity through Blockchain -- 1.7.2 IoT Devices -- 1.7.3 Data Storage and Sharing -- 1.7.4 Network Security -- 1.7.5 Navigation and Utility of the World Wide Web -- 1.7.6 Application of Blockchain in Cybersecurity -- 1.7.7 Secure Domain Name Service -- 1.7.8 Keyless Signature Infrastructure -- 1.7.9 Secured Storage -- 1.7.10 Gaps and Resolutions of Security Issues in Blockchain -- 1.7.11 Quantum Computing -- 1.7.12 Dealing with Inexperienced Users -- 1.7.13 User Anonymity -- 1.8 Cyber Security Attacks in Blockchain -- 1.8.1 DAO Attack -- 1.8.2 Liveness Attack -- 1.8.3 Eclipse Attack -- 1.8.4 Distributed Denial of Service Attack -- 1.9 Use cases of Blockchain in Cybersecurity -- 1.9.1 Blockchain Email -- 1.9.2 Endpoint Security -- 1.9.3 Privacy -- 1.9.4 Smart Contracts -- 1.10 Integration of Big Data and Blockchain -- 1.10.1 Big Data and Blockchain in E-Governance -- 1.10.1.1 Advantages -- 1.10.1.1.1 Enhancement in Quality -- 1.10.1.1.2 Ease of Access -- 1.10.1.1.3 Strengthening Trust -- 1.10.1.2 Framework for Secured E-Governance -- 1.10.2 Big Data and Blockchain in Health Care -- 1.10.3 Personal Big Data Management Using Blockchain -- 1.10.4 Big Data, Blockchain, and Cryptocurrency -- 1.10.5 Big Data and Blockchain in Fog-Enabled IoT Applications -- 1.11 Conclusion -- References | |
| 505 | 8 | |a 2. Deep Learning Techniques for Cybersecurity Applications -- 2.1 Introduction -- 2.2 Artificial Intelligence with Machine Learning and Deep Learning -- 2.3 Deep Learning and Neural Network -- 2.4 Cybersecurity -- 2.4.1 Elements of Cyber Encompass -- 2.5 DL Algorithms for Cybersecurity -- 2.5.1 Supervised Deep Learning Algorithms -- 2.5.2 Unsupervised Deep Learning Algorithms -- 2.6 Cybersecurity Use Cases -- 2.6.1 Intrusion Detection -- 2.6.2 Malware Detection -- 2.6.3 Android Malware Detection -- 2.6.4 Domain Name Categorization -- 2.6.5 Analysis of Phishing and Spamming -- 2.6.6 Traffic Investigation -- 2.6.7 Binary Exploration -- 2.7 DL Methods for Cyberattack Detection -- 2.7.1 CNN Methods -- 2.7.2 RNN Methods -- 2.7.3 RBM Methods -- 2.7.4 DBN Methods -- 2.7.5 Autoencoder -- 2.8 Cybersecurity Threats and Attacks -- 2.8.1 Malware -- 2.8.2 Phishing -- 2.8.3 MitM -- 2.8.4 SQL Injection -- 2.8.5 Zero-Day Exploit -- 2.8.6 Tunneling of DNS -- 2.9 Conclusion -- References -- 3. Deep Learning Techniques for Malware Classification -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Methodology -- 3.3.1 Malware Data Set -- 3.3.2 Data Pre-Processing -- 3.3.3 The Proposed Model -- 3.4 Experiments and Results -- 3.4.1 Experimental Setup -- 3.4.2 Results -- 3.4.3 Testing -- 3.4.4 Comparison Results -- 3.5 Conclusion and Future Work -- References -- Section II: Computational Intelligence for Cybersecurity Applications -- 4. Machine Learning and Blockchain for Security Management in Banking System -- 4.1 Introduction -- 4.2 Background and Related Works -- 4.3 Blockchain and Its Benefits in Banking and Finance -- 4.3.1 Peer-to-Peer Network (P2P) -- 4.3.2 Blocks -- 4.3.3 Transactions within a Ledger -- 4.3.4 Proof-of-Work (POW) -- 4.3.5 Blockchain Towards the Banking System -- 4.4 Machine Learning-Based Secure Transaction Processing Systems -- 4.4.1 Input Raw Data | |
| 505 | 8 | |a 4.4.2 Feature Extraction -- 4.4.3 Training Algorithm -- 4.4.4 Creating a Model -- 4.4.5 Support Vector Machine (SVM) -- 4.4.6 Decision Trees -- 4.4.7 Random Forest -- 4.4.8 XGBoost -- 4.4.9 Neural Network -- 4.5 Integration of ML and Blockchain -- 4.5.1 Application Areas: Integration of ML and Blockchain -- 4.5.1.1 Recommendation System -- 4.6 The Proposed Framework -- 4.7 Future Research Directions -- 4.7.1 Processing High-Volume Data -- 4.7.2 Scalability Issues -- 4.7.3 Resource Management -- 4.8 Conclusion -- References -- 5. Machine Learning Techniques for Fault Tolerance Management -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Comparative Analysis of Existing Methodologies -- 5.3 System Architecture -- 5.3.1 Support Vector Machine (SVM) -- 5.3.2 K-Nearest Neighbor (KNN) -- 5.4 Result Analysis -- 5.5 Conclusions -- References -- 6. An Efficient Approach for Image Detection and Recognition Using Artificial Intelligence in Cyber-Physical Systems -- 6.1 Introduction -- 6.1.1 Project Background -- 6.1.2 Project Aim, Scope, and Objectives -- 6.2 Literature Review -- 6.2.1 Face Recognition from a Movie Actor's Image -- 6.3 Research Methodology -- 6.3.1 Research Methodology for Face Detection -- 6.4 HAAR Cascade Classifier -- 6.4.1 Eigenfaces Face Recognizer -- 6.4.2 Fisherfaces Face Recognizer -- 6.4.3 Local Binary Pattern Histograms (LBPH) Face Recognizer -- 6.5 System Implementation -- 6.5.1 HAAR Cascade Classifier -- 6.5.2 LBP Cascade Classifier -- 6.6 Training Data Preparation -- 6.7 Training the Face Recognizer -- 6.8 Predicting Faces -- 6.9 Test Result Analysis -- 6.9.1 Face Detection Speed and Accuracy Test -- 6.9.2 Challenges Involved with Accuracy Rate -- 6.9.3 Face Recognition Testing -- 6.10 Efficiency Comparison -- 6.10.1 Comparison within the Face Detection Classifier -- 6.10.2 Comparison within the Face Recognition Algorithm | |
| 505 | 8 | |a 6.11 Conclusion -- References -- Section III: Blockchain and Computational Intelligence for Cybersecurity Applications -- 7. Artificial Intelligence Incorporated in Business Analytics and Blockchain to Enhance Security -- 7.1 Introduction -- 7.1.1 Motivation -- 7.1.2 Chapter Organization -- 7.2 Literature Study -- 7.3 Application of Artificial Intelligence in Business Analytics -- 7.4 Blockchain Technology and the Use of AI -- 7.5 Transactions in Blockchain -- 7.6 Proof of Work in Blockchain -- 7.7 Case Study on AI Using Blockchain -- 7.8 AI in Smart Contracts and Its Testing -- 7.9 Conclusion -- References -- 8. Blockchain Solutions for Security and Privacy Issues in Smart Health Care -- 8.1 Introduction -- 8.1.1 Research Objectives -- 8.1.2 Organisation -- 8.2 Smart Health Care -- 8.2.1 Components of Smart Health Care -- 8.2.1.1 IoT and Medical Devices -- 8.2.1.2 Connectivity -- 8.2.1.3 Stakeholders -- 8.2.1.4 Supply Chain Management -- 8.2.1.5 Administration -- 8.2.1.6 Data Management -- 8.2.1.7 Services -- 8.2.2 Smart Healthcare Architecture -- 8.2.3 Research Methodology -- 8.3 Security and Privacy Requirements of Smart Health Care -- 8.3.1 Security and Privacy Requirements -- 8.3.1.1 Device Level -- 8.3.1.2 Application Level -- 8.3.1.3 Data Level -- 8.3.1.4 Network Level -- 8.4 Security and Privacy Issues in Smart Health Care -- 8.4.1 Attack Surface in Smart Health Care -- 8.4.1.1 Device Layer -- 8.4.1.2 Network Layer -- 8.4.1.3 Application Layer -- 8.4.1.4 Data Layer -- 8.4.2 Security and Privacy Issues in Smart Health Care -- 8.4.2.1 Weak Authentication and Unauthorized Access -- 8.4.2.2 Outdated Operating System and Firmware -- 8.4.2.3 Eavesdropping and Replay Attack -- 8.4.2.4 Physical Tampering of Node -- 8.4.2.5 Denial of Service -- 8.4.2.6 Social Engineering -- 8.4.2.7 Data Modification and Disclosure -- 8.4.2.8 Rerouting | |
| 505 | 8 | |a 8.4.2.9 Side-Channel Attack | |
| 520 | |a The book offers comprehensive coverage of the essential topics, including machine Learning and Deep Learning for cybersecurity, blockchain for cybersecurity and privacy, security engineering for Cyber-physical systems, AI and Data Analytics techniques for cybersecurity in smart systems, trust in digital systems | ||
| 650 | 4 | |a Computer security-Management | |
| 700 | 1 | |a Alazab, Mamoun |d 1980- |e Sonstige |0 (DE-588)1284517497 |4 oth | |
| 700 | 1 | |a Mounir, Soufyane |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Maleh, Yassine |t Computational Intelligence for Cybersecurity Management and Applications |d Milton : Taylor & Francis Group,c2023 |z 9781032335032 |
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Datensatz im Suchindex
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| author | Maleh, Yassine 1987- |
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| author_facet | Maleh, Yassine 1987- |
| author_role | aut |
| author_sort | Maleh, Yassine 1987- |
| author_variant | y m ym |
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| bvnumber | BV049293474 |
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| contents | Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Editor biographies -- Contributors -- Section I: Big Data and Computational Intelligence for Cybersecurity Management and Applications -- 1. Big Data and Blockchain for Cybersecurity Applications: Challenges and Solutions -- 1.1 Introduction -- 1.2 Benefits of Big Data Analytics for Manufacturing Internet of Things -- 1.2.1 Improving Factory Operations and Production -- 1.2.1.1 Reducing Machine Downtime -- 1.2.1.2 Improving Product Quality -- 1.2.1.3 Enhancing Supply Chain Efficiency -- 1.2.1.4 Monitoring Manufacturing Process -- 1.2.1.5 Reduction in Energy Consumption and Energy Costs -- 1.2.1.6 Reduction of Scrap Rate -- 1.3 Big Data Analytics Research in IoT: Issues and Challenges -- 1.3.1 Data Acquisition Challenges -- 1.3.2 Data Preprocessing and Storage -- 1.3.2.1 Data Integration -- 1.3.2.2 Redundancy Reduction -- 1.3.2.3 Data Cleaning and Data Compression -- 1.3.2.4 Reliability and Persistence of Data Storage -- 1.3.2.5 Scalability -- 1.3.2.6 Efficiency -- 1.3.3 Data Analytics -- 1.3.3.1 Data Temporal and Spatial Correlation -- 1.3.3.2 Efficient Data Mining Schemes -- 1.3.3.3 Privacy and Security -- 1.3.4 Uncertainty Challenges and Computational Intelligence Techniques -- 1.3.4.1 Volume -- 1.3.4.2 Variety -- 1.3.4.3 Velocity -- 1.3.4.4 Veracity -- 1.3.4.5 Value -- 1.4 Computational Intelligence Techniques -- 1.4.1 Fuzzy Logic -- 1.4.2 Evolutionary Algorithms -- 1.4.3 Artificial Neural Networks -- 1.5 Integration of Big Data with Business Intelligence -- 1.5.1 Bikers Haven Restaurant Case Study -- 1.5.1.1 Problem -- 1.5.1.2 Solution -- 1.5.1.3 Methodology -- 1.5.1.4 Results -- 1.5.2 ChangQing Drilling Company Case Study -- 1.5.2.1 Data Integration -- 1.5.2.2 Implementation of Business Intelligence -- 1.5.2.3 Discussion 1.6 Bitcoin Adoption and Rejection -- 1.6.1 Bitcoin Adoption -- 1.6.2 Bitcoin Rejection -- 1.6.2.1 Bangladesh -- 1.6.2.2 Bolivia -- 1.6.2.3 Russia -- 1.6.2.4 Vietnam -- 1.6.3 Advantages and Disadvantages -- 1.6.3.1 Advantages -- 1.6.3.1.1 Personal Data Protection -- 1.6.3.1.2 Lower Transaction Fee -- 1.6.3.1.3 Protection through Speed of Transfer -- 1.6.3.1.4 Immunity to Inflation -- 1.6.3.2 Disadvantages -- 1.6.3.2.1 Lack of Solid Anonymity -- 1.6.3.2.2 Prone to Scams -- 1.6.3.2.3 Trust -- 1.7 Blockchain in Cybersecurity -- 1.7.1 Improving Cybersecurity through Blockchain -- 1.7.2 IoT Devices -- 1.7.3 Data Storage and Sharing -- 1.7.4 Network Security -- 1.7.5 Navigation and Utility of the World Wide Web -- 1.7.6 Application of Blockchain in Cybersecurity -- 1.7.7 Secure Domain Name Service -- 1.7.8 Keyless Signature Infrastructure -- 1.7.9 Secured Storage -- 1.7.10 Gaps and Resolutions of Security Issues in Blockchain -- 1.7.11 Quantum Computing -- 1.7.12 Dealing with Inexperienced Users -- 1.7.13 User Anonymity -- 1.8 Cyber Security Attacks in Blockchain -- 1.8.1 DAO Attack -- 1.8.2 Liveness Attack -- 1.8.3 Eclipse Attack -- 1.8.4 Distributed Denial of Service Attack -- 1.9 Use cases of Blockchain in Cybersecurity -- 1.9.1 Blockchain Email -- 1.9.2 Endpoint Security -- 1.9.3 Privacy -- 1.9.4 Smart Contracts -- 1.10 Integration of Big Data and Blockchain -- 1.10.1 Big Data and Blockchain in E-Governance -- 1.10.1.1 Advantages -- 1.10.1.1.1 Enhancement in Quality -- 1.10.1.1.2 Ease of Access -- 1.10.1.1.3 Strengthening Trust -- 1.10.1.2 Framework for Secured E-Governance -- 1.10.2 Big Data and Blockchain in Health Care -- 1.10.3 Personal Big Data Management Using Blockchain -- 1.10.4 Big Data, Blockchain, and Cryptocurrency -- 1.10.5 Big Data and Blockchain in Fog-Enabled IoT Applications -- 1.11 Conclusion -- References 2. Deep Learning Techniques for Cybersecurity Applications -- 2.1 Introduction -- 2.2 Artificial Intelligence with Machine Learning and Deep Learning -- 2.3 Deep Learning and Neural Network -- 2.4 Cybersecurity -- 2.4.1 Elements of Cyber Encompass -- 2.5 DL Algorithms for Cybersecurity -- 2.5.1 Supervised Deep Learning Algorithms -- 2.5.2 Unsupervised Deep Learning Algorithms -- 2.6 Cybersecurity Use Cases -- 2.6.1 Intrusion Detection -- 2.6.2 Malware Detection -- 2.6.3 Android Malware Detection -- 2.6.4 Domain Name Categorization -- 2.6.5 Analysis of Phishing and Spamming -- 2.6.6 Traffic Investigation -- 2.6.7 Binary Exploration -- 2.7 DL Methods for Cyberattack Detection -- 2.7.1 CNN Methods -- 2.7.2 RNN Methods -- 2.7.3 RBM Methods -- 2.7.4 DBN Methods -- 2.7.5 Autoencoder -- 2.8 Cybersecurity Threats and Attacks -- 2.8.1 Malware -- 2.8.2 Phishing -- 2.8.3 MitM -- 2.8.4 SQL Injection -- 2.8.5 Zero-Day Exploit -- 2.8.6 Tunneling of DNS -- 2.9 Conclusion -- References -- 3. Deep Learning Techniques for Malware Classification -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Methodology -- 3.3.1 Malware Data Set -- 3.3.2 Data Pre-Processing -- 3.3.3 The Proposed Model -- 3.4 Experiments and Results -- 3.4.1 Experimental Setup -- 3.4.2 Results -- 3.4.3 Testing -- 3.4.4 Comparison Results -- 3.5 Conclusion and Future Work -- References -- Section II: Computational Intelligence for Cybersecurity Applications -- 4. Machine Learning and Blockchain for Security Management in Banking System -- 4.1 Introduction -- 4.2 Background and Related Works -- 4.3 Blockchain and Its Benefits in Banking and Finance -- 4.3.1 Peer-to-Peer Network (P2P) -- 4.3.2 Blocks -- 4.3.3 Transactions within a Ledger -- 4.3.4 Proof-of-Work (POW) -- 4.3.5 Blockchain Towards the Banking System -- 4.4 Machine Learning-Based Secure Transaction Processing Systems -- 4.4.1 Input Raw Data 4.4.2 Feature Extraction -- 4.4.3 Training Algorithm -- 4.4.4 Creating a Model -- 4.4.5 Support Vector Machine (SVM) -- 4.4.6 Decision Trees -- 4.4.7 Random Forest -- 4.4.8 XGBoost -- 4.4.9 Neural Network -- 4.5 Integration of ML and Blockchain -- 4.5.1 Application Areas: Integration of ML and Blockchain -- 4.5.1.1 Recommendation System -- 4.6 The Proposed Framework -- 4.7 Future Research Directions -- 4.7.1 Processing High-Volume Data -- 4.7.2 Scalability Issues -- 4.7.3 Resource Management -- 4.8 Conclusion -- References -- 5. Machine Learning Techniques for Fault Tolerance Management -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Comparative Analysis of Existing Methodologies -- 5.3 System Architecture -- 5.3.1 Support Vector Machine (SVM) -- 5.3.2 K-Nearest Neighbor (KNN) -- 5.4 Result Analysis -- 5.5 Conclusions -- References -- 6. An Efficient Approach for Image Detection and Recognition Using Artificial Intelligence in Cyber-Physical Systems -- 6.1 Introduction -- 6.1.1 Project Background -- 6.1.2 Project Aim, Scope, and Objectives -- 6.2 Literature Review -- 6.2.1 Face Recognition from a Movie Actor's Image -- 6.3 Research Methodology -- 6.3.1 Research Methodology for Face Detection -- 6.4 HAAR Cascade Classifier -- 6.4.1 Eigenfaces Face Recognizer -- 6.4.2 Fisherfaces Face Recognizer -- 6.4.3 Local Binary Pattern Histograms (LBPH) Face Recognizer -- 6.5 System Implementation -- 6.5.1 HAAR Cascade Classifier -- 6.5.2 LBP Cascade Classifier -- 6.6 Training Data Preparation -- 6.7 Training the Face Recognizer -- 6.8 Predicting Faces -- 6.9 Test Result Analysis -- 6.9.1 Face Detection Speed and Accuracy Test -- 6.9.2 Challenges Involved with Accuracy Rate -- 6.9.3 Face Recognition Testing -- 6.10 Efficiency Comparison -- 6.10.1 Comparison within the Face Detection Classifier -- 6.10.2 Comparison within the Face Recognition Algorithm 6.11 Conclusion -- References -- Section III: Blockchain and Computational Intelligence for Cybersecurity Applications -- 7. Artificial Intelligence Incorporated in Business Analytics and Blockchain to Enhance Security -- 7.1 Introduction -- 7.1.1 Motivation -- 7.1.2 Chapter Organization -- 7.2 Literature Study -- 7.3 Application of Artificial Intelligence in Business Analytics -- 7.4 Blockchain Technology and the Use of AI -- 7.5 Transactions in Blockchain -- 7.6 Proof of Work in Blockchain -- 7.7 Case Study on AI Using Blockchain -- 7.8 AI in Smart Contracts and Its Testing -- 7.9 Conclusion -- References -- 8. Blockchain Solutions for Security and Privacy Issues in Smart Health Care -- 8.1 Introduction -- 8.1.1 Research Objectives -- 8.1.2 Organisation -- 8.2 Smart Health Care -- 8.2.1 Components of Smart Health Care -- 8.2.1.1 IoT and Medical Devices -- 8.2.1.2 Connectivity -- 8.2.1.3 Stakeholders -- 8.2.1.4 Supply Chain Management -- 8.2.1.5 Administration -- 8.2.1.6 Data Management -- 8.2.1.7 Services -- 8.2.2 Smart Healthcare Architecture -- 8.2.3 Research Methodology -- 8.3 Security and Privacy Requirements of Smart Health Care -- 8.3.1 Security and Privacy Requirements -- 8.3.1.1 Device Level -- 8.3.1.2 Application Level -- 8.3.1.3 Data Level -- 8.3.1.4 Network Level -- 8.4 Security and Privacy Issues in Smart Health Care -- 8.4.1 Attack Surface in Smart Health Care -- 8.4.1.1 Device Layer -- 8.4.1.2 Network Layer -- 8.4.1.3 Application Layer -- 8.4.1.4 Data Layer -- 8.4.2 Security and Privacy Issues in Smart Health Care -- 8.4.2.1 Weak Authentication and Unauthorized Access -- 8.4.2.2 Outdated Operating System and Firmware -- 8.4.2.3 Eavesdropping and Replay Attack -- 8.4.2.4 Physical Tampering of Node -- 8.4.2.5 Denial of Service -- 8.4.2.6 Social Engineering -- 8.4.2.7 Data Modification and Disclosure -- 8.4.2.8 Rerouting 8.4.2.9 Side-Channel Attack |
| ctrlnum | (ZDB-30-PQE)EBC7214544 (ZDB-30-PAD)EBC7214544 (ZDB-89-EBL)EBL7214544 (OCoLC)1373348367 (DE-599)BVBBV049293474 |
| dewey-full | 658.478 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.478 |
| dewey-search | 658.478 |
| dewey-sort | 3658.478 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| doi_str_mv | 10.1201/9781003319917 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000zc 4500</leader><controlfield tag="001">BV049293474</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230929</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">230822s2023 xx o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781000853346</subfield><subfield code="9">978-1-00-085334-6</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781003319917</subfield><subfield code="9">978-1-00-331991-7</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1201/9781003319917</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC7214544</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC7214544</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL7214544</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1373348367</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV049293474</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-573</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">658.478</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Maleh, Yassine</subfield><subfield code="d">1987-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1190215594</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computational Intelligence for Cybersecurity Management and Applications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Milton</subfield><subfield code="b">Taylor & Francis Group</subfield><subfield code="c">2023</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xii, 235 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Advances in Cybersecurity Management Series</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Editor biographies -- Contributors -- Section I: Big Data and Computational Intelligence for Cybersecurity Management and Applications -- 1. Big Data and Blockchain for Cybersecurity Applications: Challenges and Solutions -- 1.1 Introduction -- 1.2 Benefits of Big Data Analytics for Manufacturing Internet of Things -- 1.2.1 Improving Factory Operations and Production -- 1.2.1.1 Reducing Machine Downtime -- 1.2.1.2 Improving Product Quality -- 1.2.1.3 Enhancing Supply Chain Efficiency -- 1.2.1.4 Monitoring Manufacturing Process -- 1.2.1.5 Reduction in Energy Consumption and Energy Costs -- 1.2.1.6 Reduction of Scrap Rate -- 1.3 Big Data Analytics Research in IoT: Issues and Challenges -- 1.3.1 Data Acquisition Challenges -- 1.3.2 Data Preprocessing and Storage -- 1.3.2.1 Data Integration -- 1.3.2.2 Redundancy Reduction -- 1.3.2.3 Data Cleaning and Data Compression -- 1.3.2.4 Reliability and Persistence of Data Storage -- 1.3.2.5 Scalability -- 1.3.2.6 Efficiency -- 1.3.3 Data Analytics -- 1.3.3.1 Data Temporal and Spatial Correlation -- 1.3.3.2 Efficient Data Mining Schemes -- 1.3.3.3 Privacy and Security -- 1.3.4 Uncertainty Challenges and Computational Intelligence Techniques -- 1.3.4.1 Volume -- 1.3.4.2 Variety -- 1.3.4.3 Velocity -- 1.3.4.4 Veracity -- 1.3.4.5 Value -- 1.4 Computational Intelligence Techniques -- 1.4.1 Fuzzy Logic -- 1.4.2 Evolutionary Algorithms -- 1.4.3 Artificial Neural Networks -- 1.5 Integration of Big Data with Business Intelligence -- 1.5.1 Bikers Haven Restaurant Case Study -- 1.5.1.1 Problem -- 1.5.1.2 Solution -- 1.5.1.3 Methodology -- 1.5.1.4 Results -- 1.5.2 ChangQing Drilling Company Case Study -- 1.5.2.1 Data Integration -- 1.5.2.2 Implementation of Business Intelligence -- 1.5.2.3 Discussion</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.6 Bitcoin Adoption and Rejection -- 1.6.1 Bitcoin Adoption -- 1.6.2 Bitcoin Rejection -- 1.6.2.1 Bangladesh -- 1.6.2.2 Bolivia -- 1.6.2.3 Russia -- 1.6.2.4 Vietnam -- 1.6.3 Advantages and Disadvantages -- 1.6.3.1 Advantages -- 1.6.3.1.1 Personal Data Protection -- 1.6.3.1.2 Lower Transaction Fee -- 1.6.3.1.3 Protection through Speed of Transfer -- 1.6.3.1.4 Immunity to Inflation -- 1.6.3.2 Disadvantages -- 1.6.3.2.1 Lack of Solid Anonymity -- 1.6.3.2.2 Prone to Scams -- 1.6.3.2.3 Trust -- 1.7 Blockchain in Cybersecurity -- 1.7.1 Improving Cybersecurity through Blockchain -- 1.7.2 IoT Devices -- 1.7.3 Data Storage and Sharing -- 1.7.4 Network Security -- 1.7.5 Navigation and Utility of the World Wide Web -- 1.7.6 Application of Blockchain in Cybersecurity -- 1.7.7 Secure Domain Name Service -- 1.7.8 Keyless Signature Infrastructure -- 1.7.9 Secured Storage -- 1.7.10 Gaps and Resolutions of Security Issues in Blockchain -- 1.7.11 Quantum Computing -- 1.7.12 Dealing with Inexperienced Users -- 1.7.13 User Anonymity -- 1.8 Cyber Security Attacks in Blockchain -- 1.8.1 DAO Attack -- 1.8.2 Liveness Attack -- 1.8.3 Eclipse Attack -- 1.8.4 Distributed Denial of Service Attack -- 1.9 Use cases of Blockchain in Cybersecurity -- 1.9.1 Blockchain Email -- 1.9.2 Endpoint Security -- 1.9.3 Privacy -- 1.9.4 Smart Contracts -- 1.10 Integration of Big Data and Blockchain -- 1.10.1 Big Data and Blockchain in E-Governance -- 1.10.1.1 Advantages -- 1.10.1.1.1 Enhancement in Quality -- 1.10.1.1.2 Ease of Access -- 1.10.1.1.3 Strengthening Trust -- 1.10.1.2 Framework for Secured E-Governance -- 1.10.2 Big Data and Blockchain in Health Care -- 1.10.3 Personal Big Data Management Using Blockchain -- 1.10.4 Big Data, Blockchain, and Cryptocurrency -- 1.10.5 Big Data and Blockchain in Fog-Enabled IoT Applications -- 1.11 Conclusion -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2. Deep Learning Techniques for Cybersecurity Applications -- 2.1 Introduction -- 2.2 Artificial Intelligence with Machine Learning and Deep Learning -- 2.3 Deep Learning and Neural Network -- 2.4 Cybersecurity -- 2.4.1 Elements of Cyber Encompass -- 2.5 DL Algorithms for Cybersecurity -- 2.5.1 Supervised Deep Learning Algorithms -- 2.5.2 Unsupervised Deep Learning Algorithms -- 2.6 Cybersecurity Use Cases -- 2.6.1 Intrusion Detection -- 2.6.2 Malware Detection -- 2.6.3 Android Malware Detection -- 2.6.4 Domain Name Categorization -- 2.6.5 Analysis of Phishing and Spamming -- 2.6.6 Traffic Investigation -- 2.6.7 Binary Exploration -- 2.7 DL Methods for Cyberattack Detection -- 2.7.1 CNN Methods -- 2.7.2 RNN Methods -- 2.7.3 RBM Methods -- 2.7.4 DBN Methods -- 2.7.5 Autoencoder -- 2.8 Cybersecurity Threats and Attacks -- 2.8.1 Malware -- 2.8.2 Phishing -- 2.8.3 MitM -- 2.8.4 SQL Injection -- 2.8.5 Zero-Day Exploit -- 2.8.6 Tunneling of DNS -- 2.9 Conclusion -- References -- 3. Deep Learning Techniques for Malware Classification -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Methodology -- 3.3.1 Malware Data Set -- 3.3.2 Data Pre-Processing -- 3.3.3 The Proposed Model -- 3.4 Experiments and Results -- 3.4.1 Experimental Setup -- 3.4.2 Results -- 3.4.3 Testing -- 3.4.4 Comparison Results -- 3.5 Conclusion and Future Work -- References -- Section II: Computational Intelligence for Cybersecurity Applications -- 4. Machine Learning and Blockchain for Security Management in Banking System -- 4.1 Introduction -- 4.2 Background and Related Works -- 4.3 Blockchain and Its Benefits in Banking and Finance -- 4.3.1 Peer-to-Peer Network (P2P) -- 4.3.2 Blocks -- 4.3.3 Transactions within a Ledger -- 4.3.4 Proof-of-Work (POW) -- 4.3.5 Blockchain Towards the Banking System -- 4.4 Machine Learning-Based Secure Transaction Processing Systems -- 4.4.1 Input Raw Data</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.4.2 Feature Extraction -- 4.4.3 Training Algorithm -- 4.4.4 Creating a Model -- 4.4.5 Support Vector Machine (SVM) -- 4.4.6 Decision Trees -- 4.4.7 Random Forest -- 4.4.8 XGBoost -- 4.4.9 Neural Network -- 4.5 Integration of ML and Blockchain -- 4.5.1 Application Areas: Integration of ML and Blockchain -- 4.5.1.1 Recommendation System -- 4.6 The Proposed Framework -- 4.7 Future Research Directions -- 4.7.1 Processing High-Volume Data -- 4.7.2 Scalability Issues -- 4.7.3 Resource Management -- 4.8 Conclusion -- References -- 5. Machine Learning Techniques for Fault Tolerance Management -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Comparative Analysis of Existing Methodologies -- 5.3 System Architecture -- 5.3.1 Support Vector Machine (SVM) -- 5.3.2 K-Nearest Neighbor (KNN) -- 5.4 Result Analysis -- 5.5 Conclusions -- References -- 6. An Efficient Approach for Image Detection and Recognition Using Artificial Intelligence in Cyber-Physical Systems -- 6.1 Introduction -- 6.1.1 Project Background -- 6.1.2 Project Aim, Scope, and Objectives -- 6.2 Literature Review -- 6.2.1 Face Recognition from a Movie Actor's Image -- 6.3 Research Methodology -- 6.3.1 Research Methodology for Face Detection -- 6.4 HAAR Cascade Classifier -- 6.4.1 Eigenfaces Face Recognizer -- 6.4.2 Fisherfaces Face Recognizer -- 6.4.3 Local Binary Pattern Histograms (LBPH) Face Recognizer -- 6.5 System Implementation -- 6.5.1 HAAR Cascade Classifier -- 6.5.2 LBP Cascade Classifier -- 6.6 Training Data Preparation -- 6.7 Training the Face Recognizer -- 6.8 Predicting Faces -- 6.9 Test Result Analysis -- 6.9.1 Face Detection Speed and Accuracy Test -- 6.9.2 Challenges Involved with Accuracy Rate -- 6.9.3 Face Recognition Testing -- 6.10 Efficiency Comparison -- 6.10.1 Comparison within the Face Detection Classifier -- 6.10.2 Comparison within the Face Recognition Algorithm</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">6.11 Conclusion -- References -- Section III: Blockchain and Computational Intelligence for Cybersecurity Applications -- 7. Artificial Intelligence Incorporated in Business Analytics and Blockchain to Enhance Security -- 7.1 Introduction -- 7.1.1 Motivation -- 7.1.2 Chapter Organization -- 7.2 Literature Study -- 7.3 Application of Artificial Intelligence in Business Analytics -- 7.4 Blockchain Technology and the Use of AI -- 7.5 Transactions in Blockchain -- 7.6 Proof of Work in Blockchain -- 7.7 Case Study on AI Using Blockchain -- 7.8 AI in Smart Contracts and Its Testing -- 7.9 Conclusion -- References -- 8. Blockchain Solutions for Security and Privacy Issues in Smart Health Care -- 8.1 Introduction -- 8.1.1 Research Objectives -- 8.1.2 Organisation -- 8.2 Smart Health Care -- 8.2.1 Components of Smart Health Care -- 8.2.1.1 IoT and Medical Devices -- 8.2.1.2 Connectivity -- 8.2.1.3 Stakeholders -- 8.2.1.4 Supply Chain Management -- 8.2.1.5 Administration -- 8.2.1.6 Data Management -- 8.2.1.7 Services -- 8.2.2 Smart Healthcare Architecture -- 8.2.3 Research Methodology -- 8.3 Security and Privacy Requirements of Smart Health Care -- 8.3.1 Security and Privacy Requirements -- 8.3.1.1 Device Level -- 8.3.1.2 Application Level -- 8.3.1.3 Data Level -- 8.3.1.4 Network Level -- 8.4 Security and Privacy Issues in Smart Health Care -- 8.4.1 Attack Surface in Smart Health Care -- 8.4.1.1 Device Layer -- 8.4.1.2 Network Layer -- 8.4.1.3 Application Layer -- 8.4.1.4 Data Layer -- 8.4.2 Security and Privacy Issues in Smart Health Care -- 8.4.2.1 Weak Authentication and Unauthorized Access -- 8.4.2.2 Outdated Operating System and Firmware -- 8.4.2.3 Eavesdropping and Replay Attack -- 8.4.2.4 Physical Tampering of Node -- 8.4.2.5 Denial of Service -- 8.4.2.6 Social Engineering -- 8.4.2.7 Data Modification and Disclosure -- 8.4.2.8 Rerouting</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.4.2.9 Side-Channel Attack</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The book offers comprehensive coverage of the essential topics, including machine Learning and Deep Learning for cybersecurity, blockchain for cybersecurity and privacy, security engineering for Cyber-physical systems, AI and Data Analytics techniques for cybersecurity in smart systems, trust in digital systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer security-Management</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Alazab, Mamoun</subfield><subfield code="d">1980-</subfield><subfield code="e">Sonstige</subfield><subfield code="0">(DE-588)1284517497</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mounir, Soufyane</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Maleh, Yassine</subfield><subfield code="t">Computational Intelligence for Cybersecurity Management and Applications</subfield><subfield code="d">Milton : Taylor & Francis Group,c2023</subfield><subfield code="z">9781032335032</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1201/9781003319917</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-7-TFC</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034554825</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1201/9781003319917</subfield><subfield code="l">DE-573</subfield><subfield code="p">ZDB-7-TFC</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV049293474 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:37:47Z |
| indexdate | 2024-12-20T11:04:23Z |
| institution | BVB |
| isbn | 9781000853346 9781003319917 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034554825 |
| oclc_num | 1373348367 |
| open_access_boolean | |
| owner | DE-573 |
| owner_facet | DE-573 |
| physical | 1 Online-Ressource (xii, 235 Seiten) |
| psigel | ZDB-30-PQE ZDB-7-TFC |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Taylor & Francis Group |
| record_format | marc |
| series2 | Advances in Cybersecurity Management Series |
| spelling | Maleh, Yassine 1987- Verfasser (DE-588)1190215594 aut Computational Intelligence for Cybersecurity Management and Applications Milton Taylor & Francis Group 2023 ©2023 1 Online-Ressource (xii, 235 Seiten) txt rdacontent c rdamedia cr rdacarrier Advances in Cybersecurity Management Series Description based on publisher supplied metadata and other sources Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Editor biographies -- Contributors -- Section I: Big Data and Computational Intelligence for Cybersecurity Management and Applications -- 1. Big Data and Blockchain for Cybersecurity Applications: Challenges and Solutions -- 1.1 Introduction -- 1.2 Benefits of Big Data Analytics for Manufacturing Internet of Things -- 1.2.1 Improving Factory Operations and Production -- 1.2.1.1 Reducing Machine Downtime -- 1.2.1.2 Improving Product Quality -- 1.2.1.3 Enhancing Supply Chain Efficiency -- 1.2.1.4 Monitoring Manufacturing Process -- 1.2.1.5 Reduction in Energy Consumption and Energy Costs -- 1.2.1.6 Reduction of Scrap Rate -- 1.3 Big Data Analytics Research in IoT: Issues and Challenges -- 1.3.1 Data Acquisition Challenges -- 1.3.2 Data Preprocessing and Storage -- 1.3.2.1 Data Integration -- 1.3.2.2 Redundancy Reduction -- 1.3.2.3 Data Cleaning and Data Compression -- 1.3.2.4 Reliability and Persistence of Data Storage -- 1.3.2.5 Scalability -- 1.3.2.6 Efficiency -- 1.3.3 Data Analytics -- 1.3.3.1 Data Temporal and Spatial Correlation -- 1.3.3.2 Efficient Data Mining Schemes -- 1.3.3.3 Privacy and Security -- 1.3.4 Uncertainty Challenges and Computational Intelligence Techniques -- 1.3.4.1 Volume -- 1.3.4.2 Variety -- 1.3.4.3 Velocity -- 1.3.4.4 Veracity -- 1.3.4.5 Value -- 1.4 Computational Intelligence Techniques -- 1.4.1 Fuzzy Logic -- 1.4.2 Evolutionary Algorithms -- 1.4.3 Artificial Neural Networks -- 1.5 Integration of Big Data with Business Intelligence -- 1.5.1 Bikers Haven Restaurant Case Study -- 1.5.1.1 Problem -- 1.5.1.2 Solution -- 1.5.1.3 Methodology -- 1.5.1.4 Results -- 1.5.2 ChangQing Drilling Company Case Study -- 1.5.2.1 Data Integration -- 1.5.2.2 Implementation of Business Intelligence -- 1.5.2.3 Discussion 1.6 Bitcoin Adoption and Rejection -- 1.6.1 Bitcoin Adoption -- 1.6.2 Bitcoin Rejection -- 1.6.2.1 Bangladesh -- 1.6.2.2 Bolivia -- 1.6.2.3 Russia -- 1.6.2.4 Vietnam -- 1.6.3 Advantages and Disadvantages -- 1.6.3.1 Advantages -- 1.6.3.1.1 Personal Data Protection -- 1.6.3.1.2 Lower Transaction Fee -- 1.6.3.1.3 Protection through Speed of Transfer -- 1.6.3.1.4 Immunity to Inflation -- 1.6.3.2 Disadvantages -- 1.6.3.2.1 Lack of Solid Anonymity -- 1.6.3.2.2 Prone to Scams -- 1.6.3.2.3 Trust -- 1.7 Blockchain in Cybersecurity -- 1.7.1 Improving Cybersecurity through Blockchain -- 1.7.2 IoT Devices -- 1.7.3 Data Storage and Sharing -- 1.7.4 Network Security -- 1.7.5 Navigation and Utility of the World Wide Web -- 1.7.6 Application of Blockchain in Cybersecurity -- 1.7.7 Secure Domain Name Service -- 1.7.8 Keyless Signature Infrastructure -- 1.7.9 Secured Storage -- 1.7.10 Gaps and Resolutions of Security Issues in Blockchain -- 1.7.11 Quantum Computing -- 1.7.12 Dealing with Inexperienced Users -- 1.7.13 User Anonymity -- 1.8 Cyber Security Attacks in Blockchain -- 1.8.1 DAO Attack -- 1.8.2 Liveness Attack -- 1.8.3 Eclipse Attack -- 1.8.4 Distributed Denial of Service Attack -- 1.9 Use cases of Blockchain in Cybersecurity -- 1.9.1 Blockchain Email -- 1.9.2 Endpoint Security -- 1.9.3 Privacy -- 1.9.4 Smart Contracts -- 1.10 Integration of Big Data and Blockchain -- 1.10.1 Big Data and Blockchain in E-Governance -- 1.10.1.1 Advantages -- 1.10.1.1.1 Enhancement in Quality -- 1.10.1.1.2 Ease of Access -- 1.10.1.1.3 Strengthening Trust -- 1.10.1.2 Framework for Secured E-Governance -- 1.10.2 Big Data and Blockchain in Health Care -- 1.10.3 Personal Big Data Management Using Blockchain -- 1.10.4 Big Data, Blockchain, and Cryptocurrency -- 1.10.5 Big Data and Blockchain in Fog-Enabled IoT Applications -- 1.11 Conclusion -- References 2. Deep Learning Techniques for Cybersecurity Applications -- 2.1 Introduction -- 2.2 Artificial Intelligence with Machine Learning and Deep Learning -- 2.3 Deep Learning and Neural Network -- 2.4 Cybersecurity -- 2.4.1 Elements of Cyber Encompass -- 2.5 DL Algorithms for Cybersecurity -- 2.5.1 Supervised Deep Learning Algorithms -- 2.5.2 Unsupervised Deep Learning Algorithms -- 2.6 Cybersecurity Use Cases -- 2.6.1 Intrusion Detection -- 2.6.2 Malware Detection -- 2.6.3 Android Malware Detection -- 2.6.4 Domain Name Categorization -- 2.6.5 Analysis of Phishing and Spamming -- 2.6.6 Traffic Investigation -- 2.6.7 Binary Exploration -- 2.7 DL Methods for Cyberattack Detection -- 2.7.1 CNN Methods -- 2.7.2 RNN Methods -- 2.7.3 RBM Methods -- 2.7.4 DBN Methods -- 2.7.5 Autoencoder -- 2.8 Cybersecurity Threats and Attacks -- 2.8.1 Malware -- 2.8.2 Phishing -- 2.8.3 MitM -- 2.8.4 SQL Injection -- 2.8.5 Zero-Day Exploit -- 2.8.6 Tunneling of DNS -- 2.9 Conclusion -- References -- 3. Deep Learning Techniques for Malware Classification -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Methodology -- 3.3.1 Malware Data Set -- 3.3.2 Data Pre-Processing -- 3.3.3 The Proposed Model -- 3.4 Experiments and Results -- 3.4.1 Experimental Setup -- 3.4.2 Results -- 3.4.3 Testing -- 3.4.4 Comparison Results -- 3.5 Conclusion and Future Work -- References -- Section II: Computational Intelligence for Cybersecurity Applications -- 4. Machine Learning and Blockchain for Security Management in Banking System -- 4.1 Introduction -- 4.2 Background and Related Works -- 4.3 Blockchain and Its Benefits in Banking and Finance -- 4.3.1 Peer-to-Peer Network (P2P) -- 4.3.2 Blocks -- 4.3.3 Transactions within a Ledger -- 4.3.4 Proof-of-Work (POW) -- 4.3.5 Blockchain Towards the Banking System -- 4.4 Machine Learning-Based Secure Transaction Processing Systems -- 4.4.1 Input Raw Data 4.4.2 Feature Extraction -- 4.4.3 Training Algorithm -- 4.4.4 Creating a Model -- 4.4.5 Support Vector Machine (SVM) -- 4.4.6 Decision Trees -- 4.4.7 Random Forest -- 4.4.8 XGBoost -- 4.4.9 Neural Network -- 4.5 Integration of ML and Blockchain -- 4.5.1 Application Areas: Integration of ML and Blockchain -- 4.5.1.1 Recommendation System -- 4.6 The Proposed Framework -- 4.7 Future Research Directions -- 4.7.1 Processing High-Volume Data -- 4.7.2 Scalability Issues -- 4.7.3 Resource Management -- 4.8 Conclusion -- References -- 5. Machine Learning Techniques for Fault Tolerance Management -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Comparative Analysis of Existing Methodologies -- 5.3 System Architecture -- 5.3.1 Support Vector Machine (SVM) -- 5.3.2 K-Nearest Neighbor (KNN) -- 5.4 Result Analysis -- 5.5 Conclusions -- References -- 6. An Efficient Approach for Image Detection and Recognition Using Artificial Intelligence in Cyber-Physical Systems -- 6.1 Introduction -- 6.1.1 Project Background -- 6.1.2 Project Aim, Scope, and Objectives -- 6.2 Literature Review -- 6.2.1 Face Recognition from a Movie Actor's Image -- 6.3 Research Methodology -- 6.3.1 Research Methodology for Face Detection -- 6.4 HAAR Cascade Classifier -- 6.4.1 Eigenfaces Face Recognizer -- 6.4.2 Fisherfaces Face Recognizer -- 6.4.3 Local Binary Pattern Histograms (LBPH) Face Recognizer -- 6.5 System Implementation -- 6.5.1 HAAR Cascade Classifier -- 6.5.2 LBP Cascade Classifier -- 6.6 Training Data Preparation -- 6.7 Training the Face Recognizer -- 6.8 Predicting Faces -- 6.9 Test Result Analysis -- 6.9.1 Face Detection Speed and Accuracy Test -- 6.9.2 Challenges Involved with Accuracy Rate -- 6.9.3 Face Recognition Testing -- 6.10 Efficiency Comparison -- 6.10.1 Comparison within the Face Detection Classifier -- 6.10.2 Comparison within the Face Recognition Algorithm 6.11 Conclusion -- References -- Section III: Blockchain and Computational Intelligence for Cybersecurity Applications -- 7. Artificial Intelligence Incorporated in Business Analytics and Blockchain to Enhance Security -- 7.1 Introduction -- 7.1.1 Motivation -- 7.1.2 Chapter Organization -- 7.2 Literature Study -- 7.3 Application of Artificial Intelligence in Business Analytics -- 7.4 Blockchain Technology and the Use of AI -- 7.5 Transactions in Blockchain -- 7.6 Proof of Work in Blockchain -- 7.7 Case Study on AI Using Blockchain -- 7.8 AI in Smart Contracts and Its Testing -- 7.9 Conclusion -- References -- 8. Blockchain Solutions for Security and Privacy Issues in Smart Health Care -- 8.1 Introduction -- 8.1.1 Research Objectives -- 8.1.2 Organisation -- 8.2 Smart Health Care -- 8.2.1 Components of Smart Health Care -- 8.2.1.1 IoT and Medical Devices -- 8.2.1.2 Connectivity -- 8.2.1.3 Stakeholders -- 8.2.1.4 Supply Chain Management -- 8.2.1.5 Administration -- 8.2.1.6 Data Management -- 8.2.1.7 Services -- 8.2.2 Smart Healthcare Architecture -- 8.2.3 Research Methodology -- 8.3 Security and Privacy Requirements of Smart Health Care -- 8.3.1 Security and Privacy Requirements -- 8.3.1.1 Device Level -- 8.3.1.2 Application Level -- 8.3.1.3 Data Level -- 8.3.1.4 Network Level -- 8.4 Security and Privacy Issues in Smart Health Care -- 8.4.1 Attack Surface in Smart Health Care -- 8.4.1.1 Device Layer -- 8.4.1.2 Network Layer -- 8.4.1.3 Application Layer -- 8.4.1.4 Data Layer -- 8.4.2 Security and Privacy Issues in Smart Health Care -- 8.4.2.1 Weak Authentication and Unauthorized Access -- 8.4.2.2 Outdated Operating System and Firmware -- 8.4.2.3 Eavesdropping and Replay Attack -- 8.4.2.4 Physical Tampering of Node -- 8.4.2.5 Denial of Service -- 8.4.2.6 Social Engineering -- 8.4.2.7 Data Modification and Disclosure -- 8.4.2.8 Rerouting 8.4.2.9 Side-Channel Attack The book offers comprehensive coverage of the essential topics, including machine Learning and Deep Learning for cybersecurity, blockchain for cybersecurity and privacy, security engineering for Cyber-physical systems, AI and Data Analytics techniques for cybersecurity in smart systems, trust in digital systems Computer security-Management Alazab, Mamoun 1980- Sonstige (DE-588)1284517497 oth Mounir, Soufyane Sonstige oth Erscheint auch als Druck-Ausgabe Maleh, Yassine Computational Intelligence for Cybersecurity Management and Applications Milton : Taylor & Francis Group,c2023 9781032335032 https://doi.org/10.1201/9781003319917 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Maleh, Yassine 1987- Computational Intelligence for Cybersecurity Management and Applications Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Editor biographies -- Contributors -- Section I: Big Data and Computational Intelligence for Cybersecurity Management and Applications -- 1. Big Data and Blockchain for Cybersecurity Applications: Challenges and Solutions -- 1.1 Introduction -- 1.2 Benefits of Big Data Analytics for Manufacturing Internet of Things -- 1.2.1 Improving Factory Operations and Production -- 1.2.1.1 Reducing Machine Downtime -- 1.2.1.2 Improving Product Quality -- 1.2.1.3 Enhancing Supply Chain Efficiency -- 1.2.1.4 Monitoring Manufacturing Process -- 1.2.1.5 Reduction in Energy Consumption and Energy Costs -- 1.2.1.6 Reduction of Scrap Rate -- 1.3 Big Data Analytics Research in IoT: Issues and Challenges -- 1.3.1 Data Acquisition Challenges -- 1.3.2 Data Preprocessing and Storage -- 1.3.2.1 Data Integration -- 1.3.2.2 Redundancy Reduction -- 1.3.2.3 Data Cleaning and Data Compression -- 1.3.2.4 Reliability and Persistence of Data Storage -- 1.3.2.5 Scalability -- 1.3.2.6 Efficiency -- 1.3.3 Data Analytics -- 1.3.3.1 Data Temporal and Spatial Correlation -- 1.3.3.2 Efficient Data Mining Schemes -- 1.3.3.3 Privacy and Security -- 1.3.4 Uncertainty Challenges and Computational Intelligence Techniques -- 1.3.4.1 Volume -- 1.3.4.2 Variety -- 1.3.4.3 Velocity -- 1.3.4.4 Veracity -- 1.3.4.5 Value -- 1.4 Computational Intelligence Techniques -- 1.4.1 Fuzzy Logic -- 1.4.2 Evolutionary Algorithms -- 1.4.3 Artificial Neural Networks -- 1.5 Integration of Big Data with Business Intelligence -- 1.5.1 Bikers Haven Restaurant Case Study -- 1.5.1.1 Problem -- 1.5.1.2 Solution -- 1.5.1.3 Methodology -- 1.5.1.4 Results -- 1.5.2 ChangQing Drilling Company Case Study -- 1.5.2.1 Data Integration -- 1.5.2.2 Implementation of Business Intelligence -- 1.5.2.3 Discussion 1.6 Bitcoin Adoption and Rejection -- 1.6.1 Bitcoin Adoption -- 1.6.2 Bitcoin Rejection -- 1.6.2.1 Bangladesh -- 1.6.2.2 Bolivia -- 1.6.2.3 Russia -- 1.6.2.4 Vietnam -- 1.6.3 Advantages and Disadvantages -- 1.6.3.1 Advantages -- 1.6.3.1.1 Personal Data Protection -- 1.6.3.1.2 Lower Transaction Fee -- 1.6.3.1.3 Protection through Speed of Transfer -- 1.6.3.1.4 Immunity to Inflation -- 1.6.3.2 Disadvantages -- 1.6.3.2.1 Lack of Solid Anonymity -- 1.6.3.2.2 Prone to Scams -- 1.6.3.2.3 Trust -- 1.7 Blockchain in Cybersecurity -- 1.7.1 Improving Cybersecurity through Blockchain -- 1.7.2 IoT Devices -- 1.7.3 Data Storage and Sharing -- 1.7.4 Network Security -- 1.7.5 Navigation and Utility of the World Wide Web -- 1.7.6 Application of Blockchain in Cybersecurity -- 1.7.7 Secure Domain Name Service -- 1.7.8 Keyless Signature Infrastructure -- 1.7.9 Secured Storage -- 1.7.10 Gaps and Resolutions of Security Issues in Blockchain -- 1.7.11 Quantum Computing -- 1.7.12 Dealing with Inexperienced Users -- 1.7.13 User Anonymity -- 1.8 Cyber Security Attacks in Blockchain -- 1.8.1 DAO Attack -- 1.8.2 Liveness Attack -- 1.8.3 Eclipse Attack -- 1.8.4 Distributed Denial of Service Attack -- 1.9 Use cases of Blockchain in Cybersecurity -- 1.9.1 Blockchain Email -- 1.9.2 Endpoint Security -- 1.9.3 Privacy -- 1.9.4 Smart Contracts -- 1.10 Integration of Big Data and Blockchain -- 1.10.1 Big Data and Blockchain in E-Governance -- 1.10.1.1 Advantages -- 1.10.1.1.1 Enhancement in Quality -- 1.10.1.1.2 Ease of Access -- 1.10.1.1.3 Strengthening Trust -- 1.10.1.2 Framework for Secured E-Governance -- 1.10.2 Big Data and Blockchain in Health Care -- 1.10.3 Personal Big Data Management Using Blockchain -- 1.10.4 Big Data, Blockchain, and Cryptocurrency -- 1.10.5 Big Data and Blockchain in Fog-Enabled IoT Applications -- 1.11 Conclusion -- References 2. Deep Learning Techniques for Cybersecurity Applications -- 2.1 Introduction -- 2.2 Artificial Intelligence with Machine Learning and Deep Learning -- 2.3 Deep Learning and Neural Network -- 2.4 Cybersecurity -- 2.4.1 Elements of Cyber Encompass -- 2.5 DL Algorithms for Cybersecurity -- 2.5.1 Supervised Deep Learning Algorithms -- 2.5.2 Unsupervised Deep Learning Algorithms -- 2.6 Cybersecurity Use Cases -- 2.6.1 Intrusion Detection -- 2.6.2 Malware Detection -- 2.6.3 Android Malware Detection -- 2.6.4 Domain Name Categorization -- 2.6.5 Analysis of Phishing and Spamming -- 2.6.6 Traffic Investigation -- 2.6.7 Binary Exploration -- 2.7 DL Methods for Cyberattack Detection -- 2.7.1 CNN Methods -- 2.7.2 RNN Methods -- 2.7.3 RBM Methods -- 2.7.4 DBN Methods -- 2.7.5 Autoencoder -- 2.8 Cybersecurity Threats and Attacks -- 2.8.1 Malware -- 2.8.2 Phishing -- 2.8.3 MitM -- 2.8.4 SQL Injection -- 2.8.5 Zero-Day Exploit -- 2.8.6 Tunneling of DNS -- 2.9 Conclusion -- References -- 3. Deep Learning Techniques for Malware Classification -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Methodology -- 3.3.1 Malware Data Set -- 3.3.2 Data Pre-Processing -- 3.3.3 The Proposed Model -- 3.4 Experiments and Results -- 3.4.1 Experimental Setup -- 3.4.2 Results -- 3.4.3 Testing -- 3.4.4 Comparison Results -- 3.5 Conclusion and Future Work -- References -- Section II: Computational Intelligence for Cybersecurity Applications -- 4. Machine Learning and Blockchain for Security Management in Banking System -- 4.1 Introduction -- 4.2 Background and Related Works -- 4.3 Blockchain and Its Benefits in Banking and Finance -- 4.3.1 Peer-to-Peer Network (P2P) -- 4.3.2 Blocks -- 4.3.3 Transactions within a Ledger -- 4.3.4 Proof-of-Work (POW) -- 4.3.5 Blockchain Towards the Banking System -- 4.4 Machine Learning-Based Secure Transaction Processing Systems -- 4.4.1 Input Raw Data 4.4.2 Feature Extraction -- 4.4.3 Training Algorithm -- 4.4.4 Creating a Model -- 4.4.5 Support Vector Machine (SVM) -- 4.4.6 Decision Trees -- 4.4.7 Random Forest -- 4.4.8 XGBoost -- 4.4.9 Neural Network -- 4.5 Integration of ML and Blockchain -- 4.5.1 Application Areas: Integration of ML and Blockchain -- 4.5.1.1 Recommendation System -- 4.6 The Proposed Framework -- 4.7 Future Research Directions -- 4.7.1 Processing High-Volume Data -- 4.7.2 Scalability Issues -- 4.7.3 Resource Management -- 4.8 Conclusion -- References -- 5. Machine Learning Techniques for Fault Tolerance Management -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Comparative Analysis of Existing Methodologies -- 5.3 System Architecture -- 5.3.1 Support Vector Machine (SVM) -- 5.3.2 K-Nearest Neighbor (KNN) -- 5.4 Result Analysis -- 5.5 Conclusions -- References -- 6. An Efficient Approach for Image Detection and Recognition Using Artificial Intelligence in Cyber-Physical Systems -- 6.1 Introduction -- 6.1.1 Project Background -- 6.1.2 Project Aim, Scope, and Objectives -- 6.2 Literature Review -- 6.2.1 Face Recognition from a Movie Actor's Image -- 6.3 Research Methodology -- 6.3.1 Research Methodology for Face Detection -- 6.4 HAAR Cascade Classifier -- 6.4.1 Eigenfaces Face Recognizer -- 6.4.2 Fisherfaces Face Recognizer -- 6.4.3 Local Binary Pattern Histograms (LBPH) Face Recognizer -- 6.5 System Implementation -- 6.5.1 HAAR Cascade Classifier -- 6.5.2 LBP Cascade Classifier -- 6.6 Training Data Preparation -- 6.7 Training the Face Recognizer -- 6.8 Predicting Faces -- 6.9 Test Result Analysis -- 6.9.1 Face Detection Speed and Accuracy Test -- 6.9.2 Challenges Involved with Accuracy Rate -- 6.9.3 Face Recognition Testing -- 6.10 Efficiency Comparison -- 6.10.1 Comparison within the Face Detection Classifier -- 6.10.2 Comparison within the Face Recognition Algorithm 6.11 Conclusion -- References -- Section III: Blockchain and Computational Intelligence for Cybersecurity Applications -- 7. Artificial Intelligence Incorporated in Business Analytics and Blockchain to Enhance Security -- 7.1 Introduction -- 7.1.1 Motivation -- 7.1.2 Chapter Organization -- 7.2 Literature Study -- 7.3 Application of Artificial Intelligence in Business Analytics -- 7.4 Blockchain Technology and the Use of AI -- 7.5 Transactions in Blockchain -- 7.6 Proof of Work in Blockchain -- 7.7 Case Study on AI Using Blockchain -- 7.8 AI in Smart Contracts and Its Testing -- 7.9 Conclusion -- References -- 8. Blockchain Solutions for Security and Privacy Issues in Smart Health Care -- 8.1 Introduction -- 8.1.1 Research Objectives -- 8.1.2 Organisation -- 8.2 Smart Health Care -- 8.2.1 Components of Smart Health Care -- 8.2.1.1 IoT and Medical Devices -- 8.2.1.2 Connectivity -- 8.2.1.3 Stakeholders -- 8.2.1.4 Supply Chain Management -- 8.2.1.5 Administration -- 8.2.1.6 Data Management -- 8.2.1.7 Services -- 8.2.2 Smart Healthcare Architecture -- 8.2.3 Research Methodology -- 8.3 Security and Privacy Requirements of Smart Health Care -- 8.3.1 Security and Privacy Requirements -- 8.3.1.1 Device Level -- 8.3.1.2 Application Level -- 8.3.1.3 Data Level -- 8.3.1.4 Network Level -- 8.4 Security and Privacy Issues in Smart Health Care -- 8.4.1 Attack Surface in Smart Health Care -- 8.4.1.1 Device Layer -- 8.4.1.2 Network Layer -- 8.4.1.3 Application Layer -- 8.4.1.4 Data Layer -- 8.4.2 Security and Privacy Issues in Smart Health Care -- 8.4.2.1 Weak Authentication and Unauthorized Access -- 8.4.2.2 Outdated Operating System and Firmware -- 8.4.2.3 Eavesdropping and Replay Attack -- 8.4.2.4 Physical Tampering of Node -- 8.4.2.5 Denial of Service -- 8.4.2.6 Social Engineering -- 8.4.2.7 Data Modification and Disclosure -- 8.4.2.8 Rerouting 8.4.2.9 Side-Channel Attack Computer security-Management |
| title | Computational Intelligence for Cybersecurity Management and Applications |
| title_auth | Computational Intelligence for Cybersecurity Management and Applications |
| title_exact_search | Computational Intelligence for Cybersecurity Management and Applications |
| title_exact_search_txtP | Computational Intelligence for Cybersecurity Management and Applications |
| title_full | Computational Intelligence for Cybersecurity Management and Applications |
| title_fullStr | Computational Intelligence for Cybersecurity Management and Applications |
| title_full_unstemmed | Computational Intelligence for Cybersecurity Management and Applications |
| title_short | Computational Intelligence for Cybersecurity Management and Applications |
| title_sort | computational intelligence for cybersecurity management and applications |
| topic | Computer security-Management |
| topic_facet | Computer security-Management |
| url | https://doi.org/10.1201/9781003319917 |
| work_keys_str_mv | AT malehyassine computationalintelligenceforcybersecuritymanagementandapplications AT alazabmamoun computationalintelligenceforcybersecuritymanagementandapplications AT mounirsoufyane computationalintelligenceforcybersecuritymanagementandapplications |