LEADER 04895nam 2200529 450 001 9910830309403321 005 20230317185302.0 010 $a1-119-83590-9 010 $a1-119-83589-5 010 $a1-119-83588-7 035 $a(MiAaPQ)EBC7127888 035 $a(Au-PeEL)EBL7127888 035 $a(CKB)25219376400041 035 $a(EXLCZ)9925219376400041 100 $a20230317d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAI and machine learning for network and security management /$fYulei Wu, Jingguo Ge and Tong Li 210 1$aPiscataway, New Jersey ;$aHoboken, New Jersey :$cIEEE Press :$cWiley,$d[2023] 210 4$dİ2023 215 $a1 online resource (338 pages) 225 1 $aIEEE Press series on networks and services management 311 08$aPrint version: Ge, Jingguo AI and Machine Learning for Network and Security Management Newark : John Wiley & Sons, Incorporated,c2022 9781119835875 320 $aIncludes bibliographical references and index. 327 $aIntro -- Table of Contents -- Title Page -- Copyright -- Author Biographies -- Preface -- Acknowledgments -- Acronyms -- 1 Introduction -- 1.1 Introduction -- 1.2 Organization of the Book -- 1.3 Conclusion -- References -- 2 When Network and Security Management Meets AI and Machine Learning -- 2.1 Introduction -- 2.2 Architecture of Machine Learning?Empowered Network and Security Management -- 2.3 Supervised Learning -- 2.4 Semisupervised and Unsupervised Learning -- 2.5 Reinforcement Learning -- 2.6 Industry Products on Network and Security Management -- 2.7 Standards on Network and Security Management -- 2.8 Projects on Network and Security Management -- 2.9 Proof?of?Concepts on Network and Security Management -- 2.10 Conclusion -- References -- Notes -- 3 Learning Network Intents for Autonomous Network Management* -- 3.1 Introduction -- 3.2 Motivation -- 3.3 The Hierarchical Representation and Learning Framework for Intention Symbols Inference -- 3.4 Experiments -- 3.5 Conclusion -- References -- Notes -- 4 Virtual Network Embedding via Hierarchical Reinforcement Learning1 -- 4.1 Introduction -- 4.2 Motivation -- 4.3 Preliminaries and Notations -- 4.4 The Framework of VNE?HRL -- 4.5 Case Study -- 4.6 Related Work -- 4.7 Conclusion -- References -- Note -- 5 Concept Drift Detection for Network Traffic Classification -- 5.1 Related Concepts of Machine Learning in Data Stream Processing -- 5.2 Using an Active Approach to Solve Concept Drift in the Intrusion Detection Field -- 5.3 Concept Drift Detector Based on CVAE -- 5.4 Deployment and Experiment in Real Networks -- 5.5 Future Research Challenges and Open Issues -- 5.6 Conclusion -- References -- Note -- 6 Online Encrypted Traffic Classification Based on Lightweight Neural Networks* -- 6.1 Introduction -- 6.2 Motivation -- 6.3 Preliminaries -- 6.4 The Proposed Lightweight Model. 327 $a6.5 Case Study -- 6.6 Related Work -- 6.7 Conclusion -- References -- Notes -- 7 Context?Aware Learning for Robust Anomaly Detection* -- 7.1 Introduction -- 7.2 Pronouns -- 7.3 The Proposed Method?-?AllRobust -- 7.4 Experiments -- 7.5 Discussion -- 7.6 Conclusion -- References -- Note -- 8 Anomaly Classification with Unknown, Imbalanced and Few Labeled Log Data -- 8.1 Introduction -- 8.2 Examples -- 8.3 Methodology -- 8.4 Experimental Results and Analysis -- 8.5 Discussion -- 8.6 Conclusion -- References -- Notes -- 9 Zero Trust Networks -- 9.1 Introduction to Zero?Trust Networks -- 9.2 Zero?Trust Network Solutions -- 9.3 Machine Learning Powered Zero Trust Networks -- 9.4 Conclusion -- References -- 10 Intelligent Network Management and Operation Systems -- 10.1 Introduction -- 10.2 Traditional Operation and Maintenance Systems -- 10.3 Security Operation and Maintenance -- 10.4 AIOps -- 10.5 Machine Learning?Based Network Security Monitoring and Management Systems -- 10.6 Conclusion -- References -- 11 Conclusions, and Research Challenges and Open Issues -- 11.1 Conclusions -- 11.2 Research Challenges and Open Issues -- References -- Index -- End User License Agreement. 410 0$aIEEE Press series on networks and services management. 606 $aComputer networks$xSecurity measures$xData processing 606 $aArtificial intelligence 606 $aMachine learning 615 0$aComputer networks$xSecurity measures$xData processing. 615 0$aArtificial intelligence. 615 0$aMachine learning. 676 $a006.3 700 $aWu$b Yulei$01619491 702 $aGe$b Jingguo 702 $aLi$b Tong 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830309403321 996 $aAI and machine learning for network and security management$93951754 997 $aUNINA