Kooperativer Bibliotheksverbund

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Format: 1 online resource (264 pages) : , illustrations.

ISBN: 9780128046104 , 0128046104 , 9780128046036 , 0128046031

Series Statement: Intelligent Engineering Systems

Note: Front Cover -- Adaptive Mobile Computing: Advances in Processing Mobile Data Sets -- Copyright -- Contents -- Contributors -- Acknowledgments -- Introduction -- Part I: Generating Mobile Data -- Chapter 1: Cloud Services for Smart City Applications -- 1. Introduction -- 2. A Short Overview on Intelligent Transportation Systems -- 3. MobiWay-Middleware Connection Hub for ITS Services -- 4. An Open-Source ITS Mobile App -- 5. The MobiWay Platform -- 6. Web Services for ITS Support -- 7. Evaluation Results -- 8. Conclusion -- References -- Chapter 2: Environment Sensors Measures Processing: Integrating Real-Time and Spreadsheet-Based Data Analysis -- 1. Introduction -- 2. Environment Sensors Measure Processing: Exploiting Data Analytics for Adaptive Real-Time Data Processing -- 2.1. Spreadsheet Data Sharing for Offline Data Analytics -- 3. OSGi Framework Based IoT Engine for Real-Time Data Collection and Data Mashup -- 3.1. Object Virtualization Layer -- 3.2. Data Mashup Layer -- 3.3. OSGi Framework Based IoT Engine Implementation -- 4. Spreadsheet Space Layer: Spreadsheet-Based Data Sharing and Collaborative Analytics -- 4.1. Platform Overview -- 4.2. Data Export and Import Mechanisms -- 4.3. Data Synchronization Mechanism -- 5. A Field Test Experiment: Deploying the Platform in a Real Case Scenario -- 6. Conclusions -- References -- Further Reading -- Chapter 3: Fusion of Heterogeneous Mobile Data, Challenges, and Solutions -- 1. Introduction -- 2. Opportunities: Application Examples -- 3. Challenges -- 3.1. Nontechnical Challenges -- 3.2. Technical Challenges -- 4. Solutions -- 4.1. Platform for Sharing and Fusing Heterogeneous Mobile Data -- 4.1.1. System requirements -- 4.1.2. Our approach: SeRAVi -- Visualization -- Data retrieval -- 4.1.3. Current status and future direction -- 4.2. Platform for Sharing and Generating Social Sensor Data. , 4.2.1. System requirements -- 4.2.2. Basic idea for sharing program codes -- 4.2.3. S3 system -- SSTD, SSFD, and SSOC -- System architecture -- 4.2.4. Comparison with existing related platforms -- 4.2.5. Current status and future plan -- 4.3. Game-Based Approach for Collecting Users High-Level Context -- 4.3.1. Basic idea -- 4.3.2. System -- System architecture -- Initialization -- Questions about contexts -- App log collection -- Game design -- 4.3.3. Current status and future plan -- 5. Conclusions -- Acknowledgments -- References -- Chapter 4: Long-Range Passive Doppler-Only Target Tracking by Single-Hydrophone Underwater Sensors with MobilityaaThis wo ... -- 1. Introduction -- 2. Background and Problem Definition -- 2.1. Problem Geometry -- 2.2. Doppler -- 2.3. Problem Definition -- 3. Target-Tracking Approaches -- 4. Simulation Results -- 5. Conclusions and Future Works -- References -- Part II: Processing Mobile Data -- Chapter 5: An Online Algorithm for Online Fraud Detection: Definition and Testing -- Chapter Points -- 1. Introduction -- 2. Fraud Detection Algorithm for Online Banking -- 3. Sample Features and Data Preprocessing -- 4. Classifier Architecture -- 5. Simple Classifiers -- 5.1. SVM Simple Classifiers -- 5.2. Behavioral Simple Classifiers -- 6. Two Layers Assignment -- 7. Classification Flow -- 8. Empirical Assessment of Metaclassifier Performance -- 8.1. Pure SVM Simple Classifiers -- 8.2. SVMs and Behavioral Simple Classifiers -- 9. Cross-Validation -- 10. Sensitivity Analysis -- 11. Further Performance Tests -- 11.1. Dataset Characteristics -- 11.2. Data Preprocessing and Choice of the Classifier -- 11.3. Cost Matrix and Cost-Based Classifier -- 12. Conclusion -- Appendix A. Brief Introduction to Adaboost -- Appendix B. Brief Introduction to SVM -- References. , Chapter 6: Introducing Ubiquity in Noninvasive Measurement Systems for Agile Processes -- 1. Introduction -- 1.1. Problem Statement -- 1.2. Literature Review -- 1.2.1. Agile processes -- 1.2.2. Process modeling -- 1.2.3. Noninvasive measurement tools -- 1.2.4. Ubiquity -- 1.3. System Design -- 1.3.1. iAgile -- 1.3.2. Bayesian model -- 1.3.3. Noninvasive measurement system -- 1.3.4. Ubiquity features -- 2. Results -- 3. Conclusions -- References -- Further Reading -- Chapter 7: Constraint-Aware Data Analysis on Mobile Devices: An Application to Human Activity Recognition on Smartphones -- Chapter Points -- 1. Introduction -- 2. Constraint-Aware Data Analysis -- 2.1. Training Constraint-Aware Classifiers -- 2.2. SLT for Selecting the Best Classifier -- 3. Application to HAR on Smartphones -- 3.1. Dealing with BAs and PTs -- 3.2. HAR Dataset with BAs and PTs -- 3.3. Algorithm Performance -- 4. Conclusions -- References -- Part III: Securing Mobile Data -- Chapter 8: How on Earth Could That Happen? An Analytical Study on Selected Mobile Data Breaches -- 1. Introduction -- 2. Attack Motivations and Incentives -- 3. Breaches by the Numbers -- 4. Recent Mobile Data Breaches -- 4.1. Autonomous Devices -- 4.2. Smart-Home Devices -- 4.3. Medical IoT Devices -- 4.4. Industrial IoT Devices -- 4.5. BYOD -- 4.6. Mobile Malware -- 4.7. Communication Protocols -- 5. Mobile Data Breaches: Insights -- 5.1. Malware is Evasive -- 5.2. Too Many Attack Surfaces -- 5.3. Obstacles -- 5.4. Risks and Patterns -- 6. Recommendations -- 7. Conclusions -- References -- Chapter 9: Understanding Information Hiding to Secure Communications and to Prevent Exfiltration of Mobile Data -- 1. Introduction -- 2. Information Hiding and Mobile Devices -- 3. Covert Channels Targeting Mobile Devices: A Review -- 3.1. Local Covert Channels -- 3.2. Air-Gapped Covert Channels. , 3.3. Network Covert Channels -- 3.4. Countermeasures and Mitigation Techniques -- 4. Detecting Covert Channels and Preventing Information Hiding in Mobile Devices -- 4.1. General Reference Architecture -- 4.2. Activity-Based Detection -- 4.3. Energy-Based Detection -- 4.3.1. Regression-based detection -- 4.3.2. Classification-based detection -- 5. Experimental Results -- 5.1. Performance Evaluation of Activity-Based Detection -- 5.2. Performance Evaluation of Energy-Based Detection -- 6. Conclusions -- References -- Chapter 10: Exploring Mobile Data Security with Energy Awareness -- 1. Introduction -- 2. Mobile Data Security Threats and Countermeasures -- 3. Mobile Security Management -- 4. Mobile Data Security with Energy Awareness -- 5. Future Trends and Conclusion -- References -- Chapter 11: Effective Security Assessment of Mobile Apps with MAVeriC: Design, Implementation, and Integration of a Unifi ... -- Chapter Points -- 1. Introduction -- 2. Related Work -- 3. Overview of the Architecture -- 4. MAVeriC Integration -- 4.1. Integration with the PI CERT -- 4.2. App Monitoring Methodology -- 5. Case Studies -- 5.1. Analyzing a Known Malware -- App submission -- Malware analysis report inspection -- Permission checking report inspection -- Reverse engineering report inspection -- 5.2. Finding New Threats -- Malware analysis report inspection -- App stimulation report inspection -- Permission checking report inspection -- Network analysis report inspection -- Reverse engineering report inspection -- Outcome -- 6. Conclusions -- Acknowledgments -- References -- Conclusion -- Index -- Back Cover.

Language: English