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 ...
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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 Experim
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