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Mining structural and behavioral patterns in smart malware / Guillermo Suarez-Tangil; (2016), nº 86
noviembre - 2016
ACKNOWLEDGMENTS ABSTRACT RESUMEN CHAPTER 1. INTRODUCTION 1.1. SMART MALWARE AND SMART DEVICES 1.2. MOTIVATION AND OBJECTIVES 1.3. CONTRIBUTIONS AND ORGANIZATION PART I. FOUNDATIONS AND TOOLS CHAPTER 2. EVOLUTION, DETECTION AND ANALYSIS OF MALWARE FOR SMART DEVICES 2.1. INTRODUCTION 2.2. SECURITY MODELS IN CURRENT SMART DEVICES 2.3. MALWARE IN SMART DEVICES: EVOLUTION, CHARACTERIZATION AND EXAMPLES 2.4. MALWARE DETECTION AND ANALYSIS CHAPTER 3. MALDROID LAB: RESEARCH MALWARE LAB FOR SMART MALWARE ANALYSIS AND DETECTION 3.1. INTRODUCTION 3.2. STATIC ANALYSIS 3.3. DYNAMIC ANALYSIS 3.4. CLOUD ANALYSIS AND CONSUMPTION METERING 3.5. ONLINET MARKETS AND MALWARE REPOSITORIES PART II. STATIC-BASED ANALYSIS CHAPTER 4. A TEXT MINING APPROACH TO ANALYZING AND CLASSIFYING CODE STRUCTURES IN MALWARE FAMILIES 4.1. INTRODUCTION 4.2. DATASET AND EXPERIMENTAL SETTING 4.3. ANALYSIS OF CODE STRUCTURES IN ANDROID MALWARE FAMILIES 4.4. MINING CODE CHUNKS IN MALWARE FAMILIES 4.5. CONCLUSIONS PART III. DYNAMIC-BASED ANALYSIS CHAPTER 5. ALTERDROID: DIFFERENTIAL FAULT ANALYSIS OF OBFUSCATED MALWARE BEHAVIOR 5.1. INTRODUCTION 5.2. A DIFFERENTIAL FAULT ANALYSIS MODEL 5.3. ALTERDROID: DIFFERENTIAL FAULT ANALYSIS OF OBFUSCATED APPS 5.4. EVALUATION 5.5. CONCLUSIONS PART IV. CLOUD-BASED ANALYSIS CHAPTER 6. POWER-AWARE ANOMALY DETECTION IN SMARTPHONES 6.1. INTRODUCTION 6.2. EXPERIMENTAL SETTING 6.3. ENERGY CONSUMPTION OF ANOMALY DETECTION COMPONENTS 6.4. DEPLOYMENT STRATEGIES AND TRADE-OFFS 6.5. CASE STUDY: A DETECTOR OF REPACKAGED MALWARE 6.6. CONCLUSIONS CHAPTER 7. DETECTING TARGETED SMARTPHONE MALWARE WITH BEHAVIOR-TRIGGERING STOCHASTIC MODELS 7.1. INTRODUCTION 7.2. BEHAVIORAL MODELS 7.3. TARGETED TESTING IN THE CLOUD 7.4. CASE STUDIES 7.5. CONCLUSIONS PART V. CONCLUSIONS, FUTURE WORK AND REFERENCES CHAPTER 8. CONCLUSIONS 8.1. CONTRIBUTIONS 8.2. OPEN ISSUES AND FUTURE WORK 8.3. RESULTS
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