Cybersecurity in intelligent networking systems:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2023
|
| Online-Zugang: | FHD01 FHI01 HFA01 Volltext Volltext |
| Beschreibung: | 1 Online-Ressource (xvi, 128 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781119784128 9781119784135 9781119784104 |
Internformat
MARC
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| 100 | 1 | |a Xu, Shengjie |d ca. 20./21. Jh. |e Verfasser |0 (DE-588)1290580650 |4 aut | |
| 245 | 1 | 0 | |a Cybersecurity in intelligent networking systems |c Shengjie Xu (San Diego State University, USA), Yi Qian (University of Nebraska-Lincoln, USA), Rose Qingyang Hu (Utah State University, USA) |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2023 | |
| 264 | 4 | |c © 2023 | |
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| 505 | 8 | |a Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Cybersecurity in the Era of Artificial Intelligence -- 1.1 Artificial Intelligence for Cybersecurity -- 1.1.1 Artificial Intelligence -- 1.1.2 Machine Learning -- 1.1.2.1 Supervised Learning -- 1.1.2.2 Unsupervised Learning -- 1.1.2.3 Semi-supervised Learning -- 1.1.2.4 Reinforcement Learning -- 1.1.3 Data-Driven Workflow for Cybersecurity -- 1.2 Key Areas and Challenges -- 1.2.1 Anomaly Detection -- 1.2.2 Trustworthy Artificial Intelligence -- 1.2.3 Privacy Preservation | |
| 505 | 8 | |a 1.3 Toolbox to Build Secure and Intelligent Systems -- 1.3.1 Machine Learning and Deep Learning -- 1.3.1.1 NumPy -- 1.3.1.2 SciPy -- 1.3.1.3 Scikit-learn -- 1.3.1.4 PyTorch -- 1.3.1.5 TensorFlow -- 1.3.2 Privacy-Preserving Machine Learning -- 1.3.2.1 Syft -- 1.3.2.2 TensorFlow Federated -- 1.3.2.3 TensorFlow Privacy -- 1.3.3 Adversarial Machine Learning -- 1.3.3.1 SecML and SecML Malware -- 1.3.3.2 Foolbox -- 1.3.3.3 CleverHans -- 1.3.3.4 Counterfit -- 1.3.3.5 MintNV -- 1.4 Data Repositories for Cybersecurity Research -- 1.4.1 NSL-KDD -- 1.4.2 UNSW-NB15 -- 1.4.3 EMBER -- 1.5 Summary -- Notes | |
| 505 | 8 | |a 2.3.4 Reinforcement Learning for Penetration Test -- 2.4 Case Study: Reinforcement Learning for Automated Post-breach Penetration Test -- 2.4.1 Literature Review -- 2.4.2 Research Idea -- 2.4.3 Training Agent Using Deep Q-Learning -- 2.5 Summary -- References -- Chapter 3 Edge Computing and Secure Edge Intelligence -- 3.1 Edge Computing -- 3.2 Key Advances in Edge Computing -- 3.2.1 Security -- 3.2.2 Reliability -- 3.2.3 Survivability -- 3.3 Secure Edge Intelligence -- 3.3.1 Background and Motivation -- 3.3.2 Design of Detection Module -- 3.3.2.1 Data Pre-processing -- 3.3.2.2 Model Learning | |
| 505 | 8 | |a 3.3.2.3 Model Updating -- 3.3.3 Challenges Against Poisoning Attacks -- 3.4 Summary -- References -- Chapter 4 Edge Intelligence for Intrusion Detection -- 4.1 Edge Cyberinfrastructure -- 4.2 Edge AI Engine -- 4.2.1 Feature Engineering -- 4.2.2 Model Learning -- 4.2.3 Model Update -- 4.2.4 Predictive Analytics -- 4.3 Threat Intelligence -- 4.4 Preliminary Study -- 4.4.1 Dataset -- 4.4.2 Environmental Setup -- 4.4.3 Performance Evaluation -- 4.4.3.1 Computational Efficiency -- 4.4.3.2 Prediction Accuracy -- 4.5 Summary -- References -- Chapter 5 Robust Intrusion Detection -- 5.1 Preliminaries | |
| 700 | 1 | |a Qian, Yi |e Verfasser |4 aut | |
| 700 | 1 | |a Hu, Rose Qingyang |e Verfasser |0 (DE-588)1170082777 |4 aut | |
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Datensatz im Suchindex
| _version_ | 1804184781425999872 |
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| author | Xu, Shengjie ca. 20./21. Jh Qian, Yi Hu, Rose Qingyang |
| author_GND | (DE-588)1290580650 (DE-588)1170082777 |
| author_facet | Xu, Shengjie ca. 20./21. Jh Qian, Yi Hu, Rose Qingyang |
| author_role | aut aut aut |
| author_sort | Xu, Shengjie ca. 20./21. Jh |
| author_variant | s x sx y q yq r q h rq rqh |
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| bvnumber | BV048639990 |
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| contents | Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Cybersecurity in the Era of Artificial Intelligence -- 1.1 Artificial Intelligence for Cybersecurity -- 1.1.1 Artificial Intelligence -- 1.1.2 Machine Learning -- 1.1.2.1 Supervised Learning -- 1.1.2.2 Unsupervised Learning -- 1.1.2.3 Semi-supervised Learning -- 1.1.2.4 Reinforcement Learning -- 1.1.3 Data-Driven Workflow for Cybersecurity -- 1.2 Key Areas and Challenges -- 1.2.1 Anomaly Detection -- 1.2.2 Trustworthy Artificial Intelligence -- 1.2.3 Privacy Preservation 1.3 Toolbox to Build Secure and Intelligent Systems -- 1.3.1 Machine Learning and Deep Learning -- 1.3.1.1 NumPy -- 1.3.1.2 SciPy -- 1.3.1.3 Scikit-learn -- 1.3.1.4 PyTorch -- 1.3.1.5 TensorFlow -- 1.3.2 Privacy-Preserving Machine Learning -- 1.3.2.1 Syft -- 1.3.2.2 TensorFlow Federated -- 1.3.2.3 TensorFlow Privacy -- 1.3.3 Adversarial Machine Learning -- 1.3.3.1 SecML and SecML Malware -- 1.3.3.2 Foolbox -- 1.3.3.3 CleverHans -- 1.3.3.4 Counterfit -- 1.3.3.5 MintNV -- 1.4 Data Repositories for Cybersecurity Research -- 1.4.1 NSL-KDD -- 1.4.2 UNSW-NB15 -- 1.4.3 EMBER -- 1.5 Summary -- Notes 2.3.4 Reinforcement Learning for Penetration Test -- 2.4 Case Study: Reinforcement Learning for Automated Post-breach Penetration Test -- 2.4.1 Literature Review -- 2.4.2 Research Idea -- 2.4.3 Training Agent Using Deep Q-Learning -- 2.5 Summary -- References -- Chapter 3 Edge Computing and Secure Edge Intelligence -- 3.1 Edge Computing -- 3.2 Key Advances in Edge Computing -- 3.2.1 Security -- 3.2.2 Reliability -- 3.2.3 Survivability -- 3.3 Secure Edge Intelligence -- 3.3.1 Background and Motivation -- 3.3.2 Design of Detection Module -- 3.3.2.1 Data Pre-processing -- 3.3.2.2 Model Learning 3.3.2.3 Model Updating -- 3.3.3 Challenges Against Poisoning Attacks -- 3.4 Summary -- References -- Chapter 4 Edge Intelligence for Intrusion Detection -- 4.1 Edge Cyberinfrastructure -- 4.2 Edge AI Engine -- 4.2.1 Feature Engineering -- 4.2.2 Model Learning -- 4.2.3 Model Update -- 4.2.4 Predictive Analytics -- 4.3 Threat Intelligence -- 4.4 Preliminary Study -- 4.4.1 Dataset -- 4.4.2 Environmental Setup -- 4.4.3 Performance Evaluation -- 4.4.3.1 Computational Efficiency -- 4.4.3.2 Prediction Accuracy -- 4.5 Summary -- References -- Chapter 5 Robust Intrusion Detection -- 5.1 Preliminaries |
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| id | DE-604.BV048639990 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:17:22Z |
| indexdate | 2024-07-10T09:44:48Z |
| institution | BVB |
| isbn | 9781119784128 9781119784135 9781119784104 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034014941 |
| oclc_num | 1357533787 |
| open_access_boolean | |
| owner | DE-1050 DE-573 DE-Aug4 |
| owner_facet | DE-1050 DE-573 DE-Aug4 |
| physical | 1 Online-Ressource (xvi, 128 Seiten) Illustrationen, Diagramme |
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| publishDate | 2023 |
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| spelling | Xu, Shengjie ca. 20./21. Jh. Verfasser (DE-588)1290580650 aut Cybersecurity in intelligent networking systems Shengjie Xu (San Diego State University, USA), Yi Qian (University of Nebraska-Lincoln, USA), Rose Qingyang Hu (Utah State University, USA) Hoboken, NJ Wiley 2023 © 2023 1 Online-Ressource (xvi, 128 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Cybersecurity in the Era of Artificial Intelligence -- 1.1 Artificial Intelligence for Cybersecurity -- 1.1.1 Artificial Intelligence -- 1.1.2 Machine Learning -- 1.1.2.1 Supervised Learning -- 1.1.2.2 Unsupervised Learning -- 1.1.2.3 Semi-supervised Learning -- 1.1.2.4 Reinforcement Learning -- 1.1.3 Data-Driven Workflow for Cybersecurity -- 1.2 Key Areas and Challenges -- 1.2.1 Anomaly Detection -- 1.2.2 Trustworthy Artificial Intelligence -- 1.2.3 Privacy Preservation 1.3 Toolbox to Build Secure and Intelligent Systems -- 1.3.1 Machine Learning and Deep Learning -- 1.3.1.1 NumPy -- 1.3.1.2 SciPy -- 1.3.1.3 Scikit-learn -- 1.3.1.4 PyTorch -- 1.3.1.5 TensorFlow -- 1.3.2 Privacy-Preserving Machine Learning -- 1.3.2.1 Syft -- 1.3.2.2 TensorFlow Federated -- 1.3.2.3 TensorFlow Privacy -- 1.3.3 Adversarial Machine Learning -- 1.3.3.1 SecML and SecML Malware -- 1.3.3.2 Foolbox -- 1.3.3.3 CleverHans -- 1.3.3.4 Counterfit -- 1.3.3.5 MintNV -- 1.4 Data Repositories for Cybersecurity Research -- 1.4.1 NSL-KDD -- 1.4.2 UNSW-NB15 -- 1.4.3 EMBER -- 1.5 Summary -- Notes 2.3.4 Reinforcement Learning for Penetration Test -- 2.4 Case Study: Reinforcement Learning for Automated Post-breach Penetration Test -- 2.4.1 Literature Review -- 2.4.2 Research Idea -- 2.4.3 Training Agent Using Deep Q-Learning -- 2.5 Summary -- References -- Chapter 3 Edge Computing and Secure Edge Intelligence -- 3.1 Edge Computing -- 3.2 Key Advances in Edge Computing -- 3.2.1 Security -- 3.2.2 Reliability -- 3.2.3 Survivability -- 3.3 Secure Edge Intelligence -- 3.3.1 Background and Motivation -- 3.3.2 Design of Detection Module -- 3.3.2.1 Data Pre-processing -- 3.3.2.2 Model Learning 3.3.2.3 Model Updating -- 3.3.3 Challenges Against Poisoning Attacks -- 3.4 Summary -- References -- Chapter 4 Edge Intelligence for Intrusion Detection -- 4.1 Edge Cyberinfrastructure -- 4.2 Edge AI Engine -- 4.2.1 Feature Engineering -- 4.2.2 Model Learning -- 4.2.3 Model Update -- 4.2.4 Predictive Analytics -- 4.3 Threat Intelligence -- 4.4 Preliminary Study -- 4.4.1 Dataset -- 4.4.2 Environmental Setup -- 4.4.3 Performance Evaluation -- 4.4.3.1 Computational Efficiency -- 4.4.3.2 Prediction Accuracy -- 4.5 Summary -- References -- Chapter 5 Robust Intrusion Detection -- 5.1 Preliminaries Qian, Yi Verfasser aut Hu, Rose Qingyang Verfasser (DE-588)1170082777 aut Erscheint auch als Druck-Ausgabe, hardback 978-1-119-78391-6 https://ieeexplore.ieee.org/book/9944078 Aggregator Volltext https://onlinelibrary.wiley.com/doi/book/10.1002/9781119784135 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Xu, Shengjie ca. 20./21. Jh Qian, Yi Hu, Rose Qingyang Cybersecurity in intelligent networking systems Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Cybersecurity in the Era of Artificial Intelligence -- 1.1 Artificial Intelligence for Cybersecurity -- 1.1.1 Artificial Intelligence -- 1.1.2 Machine Learning -- 1.1.2.1 Supervised Learning -- 1.1.2.2 Unsupervised Learning -- 1.1.2.3 Semi-supervised Learning -- 1.1.2.4 Reinforcement Learning -- 1.1.3 Data-Driven Workflow for Cybersecurity -- 1.2 Key Areas and Challenges -- 1.2.1 Anomaly Detection -- 1.2.2 Trustworthy Artificial Intelligence -- 1.2.3 Privacy Preservation 1.3 Toolbox to Build Secure and Intelligent Systems -- 1.3.1 Machine Learning and Deep Learning -- 1.3.1.1 NumPy -- 1.3.1.2 SciPy -- 1.3.1.3 Scikit-learn -- 1.3.1.4 PyTorch -- 1.3.1.5 TensorFlow -- 1.3.2 Privacy-Preserving Machine Learning -- 1.3.2.1 Syft -- 1.3.2.2 TensorFlow Federated -- 1.3.2.3 TensorFlow Privacy -- 1.3.3 Adversarial Machine Learning -- 1.3.3.1 SecML and SecML Malware -- 1.3.3.2 Foolbox -- 1.3.3.3 CleverHans -- 1.3.3.4 Counterfit -- 1.3.3.5 MintNV -- 1.4 Data Repositories for Cybersecurity Research -- 1.4.1 NSL-KDD -- 1.4.2 UNSW-NB15 -- 1.4.3 EMBER -- 1.5 Summary -- Notes 2.3.4 Reinforcement Learning for Penetration Test -- 2.4 Case Study: Reinforcement Learning for Automated Post-breach Penetration Test -- 2.4.1 Literature Review -- 2.4.2 Research Idea -- 2.4.3 Training Agent Using Deep Q-Learning -- 2.5 Summary -- References -- Chapter 3 Edge Computing and Secure Edge Intelligence -- 3.1 Edge Computing -- 3.2 Key Advances in Edge Computing -- 3.2.1 Security -- 3.2.2 Reliability -- 3.2.3 Survivability -- 3.3 Secure Edge Intelligence -- 3.3.1 Background and Motivation -- 3.3.2 Design of Detection Module -- 3.3.2.1 Data Pre-processing -- 3.3.2.2 Model Learning 3.3.2.3 Model Updating -- 3.3.3 Challenges Against Poisoning Attacks -- 3.4 Summary -- References -- Chapter 4 Edge Intelligence for Intrusion Detection -- 4.1 Edge Cyberinfrastructure -- 4.2 Edge AI Engine -- 4.2.1 Feature Engineering -- 4.2.2 Model Learning -- 4.2.3 Model Update -- 4.2.4 Predictive Analytics -- 4.3 Threat Intelligence -- 4.4 Preliminary Study -- 4.4.1 Dataset -- 4.4.2 Environmental Setup -- 4.4.3 Performance Evaluation -- 4.4.3.1 Computational Efficiency -- 4.4.3.2 Prediction Accuracy -- 4.5 Summary -- References -- Chapter 5 Robust Intrusion Detection -- 5.1 Preliminaries |
| title | Cybersecurity in intelligent networking systems |
| title_auth | Cybersecurity in intelligent networking systems |
| title_exact_search | Cybersecurity in intelligent networking systems |
| title_exact_search_txtP | Cybersecurity in intelligent networking systems |
| title_full | Cybersecurity in intelligent networking systems Shengjie Xu (San Diego State University, USA), Yi Qian (University of Nebraska-Lincoln, USA), Rose Qingyang Hu (Utah State University, USA) |
| title_fullStr | Cybersecurity in intelligent networking systems Shengjie Xu (San Diego State University, USA), Yi Qian (University of Nebraska-Lincoln, USA), Rose Qingyang Hu (Utah State University, USA) |
| title_full_unstemmed | Cybersecurity in intelligent networking systems Shengjie Xu (San Diego State University, USA), Yi Qian (University of Nebraska-Lincoln, USA), Rose Qingyang Hu (Utah State University, USA) |
| title_short | Cybersecurity in intelligent networking systems |
| title_sort | cybersecurity in intelligent networking systems |
| url | https://ieeexplore.ieee.org/book/9944078 https://onlinelibrary.wiley.com/doi/book/10.1002/9781119784135 |
| work_keys_str_mv | AT xushengjie cybersecurityinintelligentnetworkingsystems AT qianyi cybersecurityinintelligentnetworkingsystems AT huroseqingyang cybersecurityinintelligentnetworkingsystems |