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Artificial immune system : applications in computer security / / Ying Tan
Artificial immune system : applications in computer security / / Ying Tan
Autore Tan Ying <1964->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2016
Descrizione fisica 1 online resource (240 p.)
Disciplina 005.8
Soggetto topico Artificial immune systems
Computer security
Computer networks - Security measures
ISBN 1-119-07627-7
1-119-07652-8
1-119-07658-7
Classificazione COM083000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- Preface xiii -- About Author xxi -- Acknowledgements xxiii -- 1 Artificial Immune System 1 -- 1.1 Introduction 1 -- 1.2 Biological Immune System 2 -- 1.2.1 Overview 2 -- 1.2.2 Adaptive Immune Process 3 -- 1.3 Characteristics of BIS 4 -- 1.4 Artificial Immune System 6 -- 1.5 AIS Models and Algorithms 8 -- 1.5.1 Negative Selection Algorithm 8 -- 1.5.2 Clonal Selection Algorithm 9 -- 1.5.3 Immune Network Model 11 -- 1.5.4 Danger Theory 12 -- 1.5.5 Immune Concentration 13 -- 1.5.6 Other Methods 14 -- 1.6 Characteristics of AIS 15 -- 1.7 Applications of Artificial Immune System 16 -- 1.7.1 Virus Detection 16 -- 1.7.2 Spam Filtering 16 -- 1.7.3 Robots 20 -- 1.7.4 Control Engineering 21 -- 1.7.5 Fault Diagnosis 22 -- 1.7.6 Optimized Design 22 -- 1.7.7 Data Analysis 22 -- 1.8 Summary 22 -- 2 Malware Detection 27 -- 2.1 Introduction 27 -- 2.2 Malware 28 -- 2.2.1 Definition and Features 28 -- 2.2.2 The Development Phases of Malware 29 -- 2.3 Classic Malware Detection Approaches 30 -- 2.3.1 Static Techniques 31 -- 2.3.2 Dynamic Techniques 31 -- 2.3.3 Heuristics 32 -- 2.4 Immune Based Malware Detection Approaches 34 -- 2.4.1 An Overview of Artificial Immune System 34 -- 2.4.2 An Overview of Artificial Immune System for Malware Detection 35 -- 2.4.3 An Immune Based Virus Detection System Using Affinity Vectors 36 -- 2.4.4 A Hierarchical Artificial Immune Model for Virus Detection 38 -- 2.4.5 A Malware Detection Model Based on a Negative Selection Algorithm with Penalty Factor 2.5 Summary 43 -- 3 Immune Principle and Neural Networks Based Malware Detection 47 -- 3.1 Introduction 47 -- 3.2 Immune System for Malicious Executable Detection 48 -- 3.2.1 Non-self Detection Principles 48 -- 3.2.2 Anomaly Detection Based on Thickness 48 -- 3.2.3 Relationship Between Diversity of Detector Representation and Anomaly Detection Hole 48 -- 3.3 Experimental Dataset 48 -- 3.4 Malware Detection Algorithm 49 -- 3.4.1 Definition of Data Structures 49 -- 3.4.2 Detection Principle and Algorithm 49.
3.4.3 Generation of Detector Set 50 -- 3.4.4 Extraction of Anomaly Characteristics 50 -- 3.4.5 Classifier 52 -- 3.5 Experiment 52 -- 3.5.1 Experimental Procedure 53 -- 3.5.2 Experimental Results 53 -- 3.5.3 Comparison With Matthew G. Schultz's Method 55 -- 3.6 Summary 57 -- 4 Multiple-Point Bit Mutation Method of Detector Generation 59 -- 4.1 Introduction 59 -- 4.2 Current Detector Generating Algorithms 60 -- 4.3 Growth Algorithms 60 -- 4.4 Multiple Point Bit Mutation Method 62 -- 4.5 Experiments 62 -- 4.5.1 Experiments on Random Dataset 62 -- 4.5.2 Change Detection of Static Files 65 -- 4.6 Summary 65 -- 5 Malware Detection System Using Affinity Vectors 67 -- 5.1 Introduction 67 -- 5.2 Malware Detection Using Affinity Vectors 68 -- 5.2.1 Sliding Window 68 -- 5.2.2 Negative Selection 68 -- 5.2.3 Clonal Selection 69 -- 5.2.4 Distances 70 -- 5.2.5 Affinity Vector 71 -- 5.2.6 Training Classifiers with Affinity Vectors 71 -- 5.3 Evaluation of Affinity Vectors based malware detection System 73 -- 5.3.1 Dataset 73 -- 5.3.2 Length of Data Fragment 73 -- 5.3.3 Experimental Results 73 -- 5.4 Summary 74 -- 6 Hierarchical Artificial Immune Model 79 -- 6.1 Introduction 79 -- 6.2 Architecture of HAIM 80 -- 6.3 Virus Gene Library Generating Module 80 -- 6.3.1 Virus ODN Library 82 -- 6.3.2 Candidate Virus Gene Library 82 -- 6.3.3 Detecting Virus Gene Library 83 -- 6.4 Self-Nonself Classification Module 84 -- 6.4.1 Matching Degree between Two Genes 84 -- 6.4.2 Suspicious Program Detection 85 -- 6.5 Simulation Results of Hierarchical Artificial Immune Model 86 -- 6.5.1 Data Set 86 -- 6.5.2 Description of Experiments 86 -- 6.6 Summary 89 -- 7 Negative Selection Algorithm with Penalty Factor 91 -- 7.1 Introduction 91 -- 7.2 Framework of NSAPF 92 -- 7.3 Malware signature extraction module 93 -- 7.3.1 Malware Instruction Library (MIL) 93 -- 7.3.2 Malware Candidate Signature Library 94 -- 7.3.3 NSAPF and Malware Detection Signature Library 96 -- 7.4 Suspicious Program Detection Module 97.
7.4.1 Signature Matching 97 -- 7.4.2 Matching between Suspicious Programs and the MDSL 97 -- 7.4.3 Analysis of Penalty Factor 98 -- 7.5 Experiments and Analysis 99 -- 7.5.1 Experimental Datasets 99 -- 7.5.2 Experiments on Henchiri dataset 100 -- 7.5.3 Experiments on CILPKU08 Dataset 103 -- 7.5.4 Experiments on VX Heavens Dataset 104 -- 7.5.5 Parameter Analysis 104 -- 7.6 Summary 105 -- 8 Danger Feature Based Negative Selection Algorithm 107 -- 8.1 Introduction 107 -- 8.1.1 Danger Feature 107 -- 8.1.2 Framework of Danger Feature Based Negative Selection Algorithm 107 -- 8.2 DFNSA for Malware Detection 109 -- 8.2.1 Danger Feature Extraction 109 -- 8.2.2 Danger Feature Vector 110 -- 8.3 Experiments 111 -- 8.3.1 Datasets 111 -- 8.3.2 Experimental Setup 111 -- 8.3.3 Selection of Parameters 112 -- 8.3.4 Experimental Results 113 -- 8.4 Discussions 113 -- 8.4.1 Comparison of Detecting Feature Libraries 113 -- 8.4.2 Comparison of Detection Time 114 -- 8.5 Summary 114 -- 9 Immune Concentration Based Malware Detection Approaches 117 -- 9.1 Introduction 117 -- 9.2 Generation of Detector Libraries 117 -- 9.3 Construction of Feature Vector for Local Concentration 122 -- 9.4 Parameters Optimization based on Particle Swarm Optimization 124 -- 9.5 Construction of Feature Vector for Hybrid Concentration 124 -- 9.5.1 Hybrid Concentration 124 -- 9.5.2 Strategies for Definition of Local Areas 126 -- 9.5.3 HC-based Malware Detection Method 127 -- 9.5.4 Discussions 128 -- 9.6 Experiments 130 -- 9.6.1 Experiments of Local Concentration 130 -- 9.6.2 Experiments of Hybrid Concentration 138 -- 9.7 Summary 142 -- 10 Immune Cooperation Mechanism Based Learning Framework 145 -- 10.1 Introduction 145 -- 10.2 Immune Signal Cooperation Mechanism based Learning Framework 148 -- 10.3 Malware Detection Model 151 -- 10.4 Experiments of Malware Detection Model 152 -- 10.4.1 Experimental setup 152 -- 10.4.2 Selection of Parameters 153 -- 10.4.3 Experimental Results 153 -- 10.4.4 Statistical Analysis 155.
10.5 Discussions 157 -- 10.5.1 Advantages 157 -- 10.5.2 Time Complexity 157 -- 10.6 Summary 158 -- 11 Class-wise Information Gain 161 -- 11.1 Introduction 161 -- 11.2 Problem Statement 163 -- 11.2.1 Definition of the Generalized Class 163 -- 11.2.2 Malware Recognition Problem 163 -- 11.3 Class-wise Information Gain 164 -- 11.3.1 Definition 164 -- 11.3.2 Analysis 166 -- 11.4 CIG-based Malware Detection Method 170 -- 11.4.1 Feature Selection Module 170 -- 11.4.2 Classification Module 171 -- 11.5 Dataset 172 -- 11.5.1 Benign Program Dataset 172 -- 11.5.2 Malware Dataset 172 -- 11.6 Selection of Parameter 174 -- 11.6.1 Experimental Setup 174 -- 11.6.2 Experiments of Selection of Parameter 174 -- 11.7 Experimental Results 175 -- 11.7.1 Experiments on the VXHeavens Dataset 177 -- 11.7.2 Experiments on the Henchiri Dataset 179 -- 11.7.3 Experiments on the CILPKU08 Dataset 180 -- 11.8 Discussions 180 -- 11.8.1 The Relationship Among IG-A, DFCIG-B and DFCIG-M 181 -- 11.8.2 Space Complexity 182 -- 11.9 Summary 183 -- Index 185.
Record Nr. UNINA-9910135024403321
Tan Ying <1964->  
Hoboken, New Jersey : , : Wiley, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial immune system : applications in computer security / / Ying Tan
Artificial immune system : applications in computer security / / Ying Tan
Autore Tan Ying <1964->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2016
Descrizione fisica 1 online resource (240 p.)
Disciplina 005.8
Soggetto topico Artificial immune systems
Computer security
Computer networks - Security measures
ISBN 1-119-07627-7
1-119-07652-8
1-119-07658-7
Classificazione COM083000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- Preface xiii -- About Author xxi -- Acknowledgements xxiii -- 1 Artificial Immune System 1 -- 1.1 Introduction 1 -- 1.2 Biological Immune System 2 -- 1.2.1 Overview 2 -- 1.2.2 Adaptive Immune Process 3 -- 1.3 Characteristics of BIS 4 -- 1.4 Artificial Immune System 6 -- 1.5 AIS Models and Algorithms 8 -- 1.5.1 Negative Selection Algorithm 8 -- 1.5.2 Clonal Selection Algorithm 9 -- 1.5.3 Immune Network Model 11 -- 1.5.4 Danger Theory 12 -- 1.5.5 Immune Concentration 13 -- 1.5.6 Other Methods 14 -- 1.6 Characteristics of AIS 15 -- 1.7 Applications of Artificial Immune System 16 -- 1.7.1 Virus Detection 16 -- 1.7.2 Spam Filtering 16 -- 1.7.3 Robots 20 -- 1.7.4 Control Engineering 21 -- 1.7.5 Fault Diagnosis 22 -- 1.7.6 Optimized Design 22 -- 1.7.7 Data Analysis 22 -- 1.8 Summary 22 -- 2 Malware Detection 27 -- 2.1 Introduction 27 -- 2.2 Malware 28 -- 2.2.1 Definition and Features 28 -- 2.2.2 The Development Phases of Malware 29 -- 2.3 Classic Malware Detection Approaches 30 -- 2.3.1 Static Techniques 31 -- 2.3.2 Dynamic Techniques 31 -- 2.3.3 Heuristics 32 -- 2.4 Immune Based Malware Detection Approaches 34 -- 2.4.1 An Overview of Artificial Immune System 34 -- 2.4.2 An Overview of Artificial Immune System for Malware Detection 35 -- 2.4.3 An Immune Based Virus Detection System Using Affinity Vectors 36 -- 2.4.4 A Hierarchical Artificial Immune Model for Virus Detection 38 -- 2.4.5 A Malware Detection Model Based on a Negative Selection Algorithm with Penalty Factor 2.5 Summary 43 -- 3 Immune Principle and Neural Networks Based Malware Detection 47 -- 3.1 Introduction 47 -- 3.2 Immune System for Malicious Executable Detection 48 -- 3.2.1 Non-self Detection Principles 48 -- 3.2.2 Anomaly Detection Based on Thickness 48 -- 3.2.3 Relationship Between Diversity of Detector Representation and Anomaly Detection Hole 48 -- 3.3 Experimental Dataset 48 -- 3.4 Malware Detection Algorithm 49 -- 3.4.1 Definition of Data Structures 49 -- 3.4.2 Detection Principle and Algorithm 49.
3.4.3 Generation of Detector Set 50 -- 3.4.4 Extraction of Anomaly Characteristics 50 -- 3.4.5 Classifier 52 -- 3.5 Experiment 52 -- 3.5.1 Experimental Procedure 53 -- 3.5.2 Experimental Results 53 -- 3.5.3 Comparison With Matthew G. Schultz's Method 55 -- 3.6 Summary 57 -- 4 Multiple-Point Bit Mutation Method of Detector Generation 59 -- 4.1 Introduction 59 -- 4.2 Current Detector Generating Algorithms 60 -- 4.3 Growth Algorithms 60 -- 4.4 Multiple Point Bit Mutation Method 62 -- 4.5 Experiments 62 -- 4.5.1 Experiments on Random Dataset 62 -- 4.5.2 Change Detection of Static Files 65 -- 4.6 Summary 65 -- 5 Malware Detection System Using Affinity Vectors 67 -- 5.1 Introduction 67 -- 5.2 Malware Detection Using Affinity Vectors 68 -- 5.2.1 Sliding Window 68 -- 5.2.2 Negative Selection 68 -- 5.2.3 Clonal Selection 69 -- 5.2.4 Distances 70 -- 5.2.5 Affinity Vector 71 -- 5.2.6 Training Classifiers with Affinity Vectors 71 -- 5.3 Evaluation of Affinity Vectors based malware detection System 73 -- 5.3.1 Dataset 73 -- 5.3.2 Length of Data Fragment 73 -- 5.3.3 Experimental Results 73 -- 5.4 Summary 74 -- 6 Hierarchical Artificial Immune Model 79 -- 6.1 Introduction 79 -- 6.2 Architecture of HAIM 80 -- 6.3 Virus Gene Library Generating Module 80 -- 6.3.1 Virus ODN Library 82 -- 6.3.2 Candidate Virus Gene Library 82 -- 6.3.3 Detecting Virus Gene Library 83 -- 6.4 Self-Nonself Classification Module 84 -- 6.4.1 Matching Degree between Two Genes 84 -- 6.4.2 Suspicious Program Detection 85 -- 6.5 Simulation Results of Hierarchical Artificial Immune Model 86 -- 6.5.1 Data Set 86 -- 6.5.2 Description of Experiments 86 -- 6.6 Summary 89 -- 7 Negative Selection Algorithm with Penalty Factor 91 -- 7.1 Introduction 91 -- 7.2 Framework of NSAPF 92 -- 7.3 Malware signature extraction module 93 -- 7.3.1 Malware Instruction Library (MIL) 93 -- 7.3.2 Malware Candidate Signature Library 94 -- 7.3.3 NSAPF and Malware Detection Signature Library 96 -- 7.4 Suspicious Program Detection Module 97.
7.4.1 Signature Matching 97 -- 7.4.2 Matching between Suspicious Programs and the MDSL 97 -- 7.4.3 Analysis of Penalty Factor 98 -- 7.5 Experiments and Analysis 99 -- 7.5.1 Experimental Datasets 99 -- 7.5.2 Experiments on Henchiri dataset 100 -- 7.5.3 Experiments on CILPKU08 Dataset 103 -- 7.5.4 Experiments on VX Heavens Dataset 104 -- 7.5.5 Parameter Analysis 104 -- 7.6 Summary 105 -- 8 Danger Feature Based Negative Selection Algorithm 107 -- 8.1 Introduction 107 -- 8.1.1 Danger Feature 107 -- 8.1.2 Framework of Danger Feature Based Negative Selection Algorithm 107 -- 8.2 DFNSA for Malware Detection 109 -- 8.2.1 Danger Feature Extraction 109 -- 8.2.2 Danger Feature Vector 110 -- 8.3 Experiments 111 -- 8.3.1 Datasets 111 -- 8.3.2 Experimental Setup 111 -- 8.3.3 Selection of Parameters 112 -- 8.3.4 Experimental Results 113 -- 8.4 Discussions 113 -- 8.4.1 Comparison of Detecting Feature Libraries 113 -- 8.4.2 Comparison of Detection Time 114 -- 8.5 Summary 114 -- 9 Immune Concentration Based Malware Detection Approaches 117 -- 9.1 Introduction 117 -- 9.2 Generation of Detector Libraries 117 -- 9.3 Construction of Feature Vector for Local Concentration 122 -- 9.4 Parameters Optimization based on Particle Swarm Optimization 124 -- 9.5 Construction of Feature Vector for Hybrid Concentration 124 -- 9.5.1 Hybrid Concentration 124 -- 9.5.2 Strategies for Definition of Local Areas 126 -- 9.5.3 HC-based Malware Detection Method 127 -- 9.5.4 Discussions 128 -- 9.6 Experiments 130 -- 9.6.1 Experiments of Local Concentration 130 -- 9.6.2 Experiments of Hybrid Concentration 138 -- 9.7 Summary 142 -- 10 Immune Cooperation Mechanism Based Learning Framework 145 -- 10.1 Introduction 145 -- 10.2 Immune Signal Cooperation Mechanism based Learning Framework 148 -- 10.3 Malware Detection Model 151 -- 10.4 Experiments of Malware Detection Model 152 -- 10.4.1 Experimental setup 152 -- 10.4.2 Selection of Parameters 153 -- 10.4.3 Experimental Results 153 -- 10.4.4 Statistical Analysis 155.
10.5 Discussions 157 -- 10.5.1 Advantages 157 -- 10.5.2 Time Complexity 157 -- 10.6 Summary 158 -- 11 Class-wise Information Gain 161 -- 11.1 Introduction 161 -- 11.2 Problem Statement 163 -- 11.2.1 Definition of the Generalized Class 163 -- 11.2.2 Malware Recognition Problem 163 -- 11.3 Class-wise Information Gain 164 -- 11.3.1 Definition 164 -- 11.3.2 Analysis 166 -- 11.4 CIG-based Malware Detection Method 170 -- 11.4.1 Feature Selection Module 170 -- 11.4.2 Classification Module 171 -- 11.5 Dataset 172 -- 11.5.1 Benign Program Dataset 172 -- 11.5.2 Malware Dataset 172 -- 11.6 Selection of Parameter 174 -- 11.6.1 Experimental Setup 174 -- 11.6.2 Experiments of Selection of Parameter 174 -- 11.7 Experimental Results 175 -- 11.7.1 Experiments on the VXHeavens Dataset 177 -- 11.7.2 Experiments on the Henchiri Dataset 179 -- 11.7.3 Experiments on the CILPKU08 Dataset 180 -- 11.8 Discussions 180 -- 11.8.1 The Relationship Among IG-A, DFCIG-B and DFCIG-M 181 -- 11.8.2 Space Complexity 182 -- 11.9 Summary 183 -- Index 185.
Record Nr. UNINA-9910830511803321
Tan Ying <1964->  
Hoboken, New Jersey : , : Wiley, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Quantum information theory / / Mark M. Wilde, McGill University, Montréal [[electronic resource]]
Quantum information theory / / Mark M. Wilde, McGill University, Montréal [[electronic resource]]
Autore Wilde Mark <1980->
Pubbl/distr/stampa Cambridge : , : Cambridge University Press, , 2013
Descrizione fisica 1 online resource (xv, 655 pages) : digital, PDF file(s)
Disciplina 003/.54
Soggetto topico Quantum computers
Quantum communication
Information theory - Data processing
Electronic data processing - Technological innovations
ISBN 1-316-09039-6
1-107-25577-5
1-107-05712-4
1-107-05961-5
1-107-05836-8
1-107-05604-7
1-139-52534-4
Classificazione COM083000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: How to use this book; Acknowledgements; Part I. Introduction: 1. Concepts in quantum Shannon theory; 2. Classical Shannon theory; Part II. The Quantum Theory: 3. The noiseless quantum theory; 4. The noisy quantum theory; 5. The purified quantum theory; Part III. Unit Quantum Protocols: 6. Three unit quantum protocols; 7. Coherent protocols; 8. The unit resource capacity region; Part IV. Tools of Quantum Shannon Theory: 9. Distance measures; 10. Classical information and entropy; 11. Quantum information and entropy; 12. The information of quantum channels; 13. Classical typicality; 14. Quantum typicality; 15. The packing lemma; 16. The covering lemma; Part V. Noiseless Quantum Shannon Theory: 17. Schumacher compression; 18. Entanglement concentration; Part VI. Noisy Quantum Shannon Theory: 19. Classical communication; 20. Entanglement-assisted classical communication; 21. Coherent communication with noisy resources; 22. Private classical communication; 23. Quantum communication; 24. Trading resources for communication; 25. Summary and outlook; Appendix A. Miscellaneous mathematics; Appendix B. Monotonicity of quantum relative entropy; References; Index.
Record Nr. UNINA-9910786999003321
Wilde Mark <1980->  
Cambridge : , : Cambridge University Press, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Quantum information theory / / Mark M. Wilde, McGill University, Montréal [[electronic resource]]
Quantum information theory / / Mark M. Wilde, McGill University, Montréal [[electronic resource]]
Autore Wilde Mark <1980->
Pubbl/distr/stampa Cambridge : , : Cambridge University Press, , 2013
Descrizione fisica 1 online resource (xv, 655 pages) : digital, PDF file(s)
Disciplina 003/.54
Soggetto topico Quantum computers
Quantum communication
Information theory - Data processing
Electronic data processing - Technological innovations
ISBN 1-316-09039-6
1-107-25577-5
1-107-05712-4
1-107-05961-5
1-107-05836-8
1-107-05604-7
1-139-52534-4
Classificazione COM083000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: How to use this book; Acknowledgements; Part I. Introduction: 1. Concepts in quantum Shannon theory; 2. Classical Shannon theory; Part II. The Quantum Theory: 3. The noiseless quantum theory; 4. The noisy quantum theory; 5. The purified quantum theory; Part III. Unit Quantum Protocols: 6. Three unit quantum protocols; 7. Coherent protocols; 8. The unit resource capacity region; Part IV. Tools of Quantum Shannon Theory: 9. Distance measures; 10. Classical information and entropy; 11. Quantum information and entropy; 12. The information of quantum channels; 13. Classical typicality; 14. Quantum typicality; 15. The packing lemma; 16. The covering lemma; Part V. Noiseless Quantum Shannon Theory: 17. Schumacher compression; 18. Entanglement concentration; Part VI. Noisy Quantum Shannon Theory: 19. Classical communication; 20. Entanglement-assisted classical communication; 21. Coherent communication with noisy resources; 22. Private classical communication; 23. Quantum communication; 24. Trading resources for communication; 25. Summary and outlook; Appendix A. Miscellaneous mathematics; Appendix B. Monotonicity of quantum relative entropy; References; Index.
Record Nr. UNINA-9910826840003321
Wilde Mark <1980->  
Cambridge : , : Cambridge University Press, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui