LEADER 07287nam 22004573 450 001 9910632494403321 005 20221105060230.0 010 $a1-119-78413-1 010 $a1-119-78410-7 010 $a1-119-78412-3 035 $a(MiAaPQ)EBC7130612 035 $a(Au-PeEL)EBL7130612 035 $a(CKB)25270935500041 035 $a(EXLCZ)9925270935500041 100 $a20221105d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCybersecurity in Intelligent Networking Systems 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2022. 210 4$d©2023. 215 $a1 online resource (147 pages) 225 1 $aIEEE Press Ser. 311 08$aPrint version: Xu, Shengjie Cybersecurity in Intelligent Networking Systems Newark : John Wiley & Sons, Incorporated,c2022 9781119783916 327 $aCover -- 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 -- References -- Chapter 2 Cyber Threats and Gateway Defense -- 2.1 Cyber Threats -- 2.1.1 Cyber Intrusions -- 2.1.2 Distributed Denial of Services Attack -- 2.1.3 Malware and Shellcode -- 2.2 Gateway Defense Approaches -- 2.2.1 Network Access Control -- 2.2.2 Anomaly Isolation -- 2.2.3 Collaborative Learning -- 2.2.4 Secure Local Data Learning -- 2.3 Emerging Data?driven Methods for Gateway Defense -- 2.3.1 Semi?supervised Learning for Intrusion Detection -- 2.3.2 Transfer Learning for Intrusion Detection -- 2.3.3 Federated Learning for Privacy Preservation -- 2.3.4 Reinforcement Learning for Penetration Test. 327 $a2.4 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 -- 5.1.1 Median Absolute Deviation -- 5.1.2 Mahalanobis Distance -- 5.2 Robust Intrusion Detection -- 5.2.1 Problem Formulation -- 5.2.2 Step 1: Robust Data Pre?processing -- 5.2.3 Step 2: Bagging for Labeled Anomalies -- 5.2.4 Step 3: One?class SVM for Unlabeled Samples -- 5.2.4.1 One?class Classification -- 5.2.4.2 Algorithm of Optimal Sampling Ratio Section -- 5.2.5 Step 4: The Final Classifier -- 5.3 Experimental and Evaluation -- 5.3.1 Experiment Setup -- 5.3.1.1 Datasets -- 5.3.1.2 Environmental Setup -- 5.3.1.3 Evaluation Metrics -- 5.3.2 Performance Evaluation -- 5.3.2.1 Step 1 -- 5.3.2.2 Step 2 -- 5.3.2.3 Step 3 -- 5.3.2.4 Step 4 -- 5.4 Summary -- References. 327 $aChapter 6 Efficient Pre?processing Scheme for Anomaly Detection -- 6.1 Efficient Anomaly Detection -- 6.1.1 Related Work -- 6.1.2 Principal Component Analysis -- 6.2 Proposed Pre?processing Scheme for Anomaly Detection -- 6.2.1 Robust Pre?processing Scheme -- 6.2.2 Real?Time Processing -- 6.2.3 Discussion -- 6.3 Case Study -- 6.3.1 Description of the Raw Data -- 6.3.1.1 Dimension -- 6.3.1.2 Predictors -- 6.3.1.3 Response Variables -- 6.3.2 Experiment -- 6.3.3 Results -- 6.4 Summary -- References -- Chapter 7 Privacy Preservation in the Era of Big Data -- 7.1 Privacy Preservation Approaches -- 7.1.1 Anonymization -- 7.1.2 Differential Privacy -- 7.1.3 Federated Learning -- 7.1.4 Homomorphic Encryption -- 7.1.5 Secure Multi?party Computation -- 7.1.6 Discussion -- 7.2 Privacy?Preserving Anomaly Detection -- 7.2.1 Literature Review -- 7.2.2 Preliminaries -- 7.2.2.1 Bilinear Groups -- 7.2.2.2 Asymmetric Predicate Encryption -- 7.2.3 System Model and Security Model -- 7.2.3.1 System Model -- 7.2.3.2 Security Model -- 7.3 Objectives and Workflow -- 7.3.1 Objectives -- 7.3.2 Workflow -- 7.4 Predicate Encryption?Based Anomaly Detection -- 7.4.1 Procedures -- 7.4.2 Development of Predicate -- 7.4.3 Deployment of Anomaly Detection -- 7.5 Case Study and Evaluation -- 7.5.1 Overhead -- 7.5.2 Detection -- 7.6 Summary -- References -- Chapter 8 Adversarial Examples: Challenges and Solutions -- 8.1 Adversarial Examples -- 8.1.1 Problem Formulation in Machine Learning -- 8.1.2 Creation of Adversarial Examples -- 8.1.3 Targeted and Non?targeted Attacks -- 8.1.4 Black?box and White?box Attacks -- 8.1.5 Defenses Against Adversarial Examples -- 8.2 Adversarial Attacks in Security Applications -- 8.2.1 Malware -- 8.2.2 Cyber Intrusions -- 8.3 Case Study: Improving Adversarial Attacks Against Malware Detectors -- 8.3.1 Background. 327 $a8.3.2 Adversarial Attacks on Malware Detectors -- 8.3.3 MalConv Architecture -- 8.3.4 Research Idea -- 8.4 Case Study: A Metric for Machine Learning Vulnerability to Adversarial Examples -- 8.4.1 Background -- 8.4.2 Research Idea -- 8.5 Case Study: Protecting Smart Speakers from Adversarial Voice Commands -- 8.5.1 Background -- 8.5.2 Challenges -- 8.5.3 Directions and Tasks -- 8.6 Summary -- References -- Index -- EULA. 410 0$aIEEE Press Ser. 700 $aXu$b Shengjie$01268308 701 $aQian$b Yi$01268309 701 $aHu$b Rose Qingyang$0989115 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910632494403321 996 $aCybersecurity in Intelligent Networking Systems$92983080 997 $aUNINA LEADER 01084nam a2200289 i 4500 001 991003241919707536 005 20020509114700.0 008 010514s1966 it ||| | ita 035 $ab11130908-39ule_inst 035 $aPARLA178030$9ExL 040 $aDip.to Filosofia$bita 082 0 $a193 100 1 $aKelkel, Lothar$0159948 245 10$aHusserl :$bla vita e l'opera /$cLothar Kelkel, René Scherer ; traduzione e appendice bibliografica a cura di Emilio renzi 260 $aMilano :$bIl Saggiatore,$c1966 300 $a181 p. ;$c17 cm. 440 2$aI gabbiani ;$v43 500 $aTit. orig.: Husserl, sa vie, son oeuvre 650 4$aHusserl, Edmund 700 1 $aScherer, René$eauthor$4http://id.loc.gov/vocabulary/relators/aut$0385730 700 1 $aRenzi, Emilio 907 $a.b11130908$b02-04-14$c28-06-02 912 $a991003241919707536 945 $aLE005IF XXXIII A 54$g1$iLE005IFA-3045$lle005$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i11269558$z28-06-02 996 $aHusserl$924839 997 $aUNISALENTO 998 $ale005$b01-01-01$cm$da $e-$fita$git $h0$i1 LEADER 03191nam 2200625Ia 450 001 996208216203316 005 20240524212003.0 010 $a1-281-30926-5 010 $a9786611309268 010 $a0-470-69234-0 010 $a0-470-69156-5 035 $a(CKB)1000000000399760 035 $a(EBL)351120 035 $a(SSID)ssj0000149479 035 $a(PQKBManifestationID)11147448 035 $a(PQKBTitleCode)TC0000149479 035 $a(PQKBWorkID)10237616 035 $a(PQKB)10247328 035 $a(MiAaPQ)EBC351120 035 $a(OCoLC)232611674 035 $a(EXLCZ)991000000000399760 100 $a20070503d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEssentials of avian medicine and surgery$b[electronic resource] /$fBrian H. Coles ; with contributions from Maria Krautwald-Junghanns, Susan E. Orosz, Thomas N. Tully 205 $a3rd ed. 210 $aOxford ;$aAmes, Iowa $cBlackwell Pub.$d2007 215 $a1 online resource (415 p.) 300 $aRev. ed. of: Avian medicine and surgery / B.H. Coles. 2nd ed. 1997. 311 $a1-4051-5755-0 320 $aIncludes bibliographical references (p. 363-379) and index. 327 $aEssentials of Avian Medicine and Surgery; Contents; Preface; 1: Diversity in Anatomy and Physiology: Clinical Significance; 2: The Special Senses of Birds; 3: Clinical Examination; 4: Aids to Diagnosis; 5: Post-mortem Examination; 6: Medication and Administration of Drugs; 7: Anaesthesia; 8: Surgery; 9: Nursing and After Care; Colour Plates; 10: Breeding Problems; 11: Release of Casualty Wild Birds; Appendices; 1 An avian formulary; 2 Bacterial diseases of birds; 3 Viral diseases of birds; 4 Mycotic diseases of birds; 5 Parasitic diseases of birds; 6 Poisons likely to affect birds 327 $a7 Some suggested diagnostic schedules8 Weights of birds most likely to be seen in general practice; 9 Incubation and . edging periods of selected birds; 10 Glossary; 11 Some useful websites; Further Reading; References; Index 330 $aEssentials of Avian Medicine and Surgery is designed as a concise quick reference for the busy practitioner and animal nurse. 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