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Data Mining Trends and Applications in Criminal Science and Investigations

Book Description

The field of data mining is receiving significant attention in today's information-rich society, where data is available from different sources and formats, in large volumes, and no longer constitutes a bottleneck for knowledge acquisition. This rich information has paved the way for novel areas of research, particularly in the crime data analysis realm. Data Mining Trends and Applications in Criminal Science and Investigations presents scientific concepts and frameworks of data mining and analytics implementation and uses across various domains, such as public safety, criminal investigations, intrusion detection, crime scene analysis, and suspect modeling. Exploring the diverse ways that data is revolutionizing the field of criminal science, this publication meets the research needs of law enforcement professionals, data analysts, investigators, researchers, and graduate-level students.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  6. Foreword
  7. Preface
    1. CHALLENGES AND OPPORTUNITIES
    2. ORGANIZATION OF THE BOOK
    3. REFERENCES
  8. Acknowledgment
  9. Section 1: Challenges and Existing Strategies in Public Safety and Crime Mining
    1. Chapter 1: On the Advancement of Using Data Mining for Crime Situation Recognition
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND CONCEPTS: OVERVIEW OF CRIME DATA MINING
      4. 3.0 CURRENT IMPLEMENTATION TO CRIME DATA MINING
      5. 4.0 RESEARCH ISSUES IN CRIME DATA MINING FOR SITUATION RECOGNITION
      6. 5.0 SUMMARY AND CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
      10. APPENDIX: LIST OF NOTATIONS
    2. Chapter 2: A Classification Framework for Data Mining Applications in Criminal Science and Investigations
      1. ABSTRACT
      2. INTRODUCTION
      3. RESEARCH DESIGN
      4. CLASSIFICATION FRAMEWORK FOR DATA MINING TECHNIQUES IN CRIME PATTERN DETECTION
      5. DATA MINING BASED LAYERED FRAMEWORK FOR CRIME INVESTIGATION
      6. OPPORTUNITIES OF DATA MINING TECHNIQUES ASSOCIATED WITH THE CRIMINAL SCIENCE AND INVESTIGATION.
      7. CHALLENGES OF DATA MINING ASSOCIATED WITH THE CRIMINAL SCIENCE AND INVESTIGATION
      8. CLASSIFICATION OF PAPERS
      9. IMPLICATION OF RESEARCH AND IMPLEMENTATION
      10. LIMITATION
      11. FUTURE RESEARCH DIRECTIONS
      12. CONCLUSION
      13. REFERENCES
      14. ADDITIONAL READING
      15. KEY TERMS AND DEFINITIONS
  10. Section 2: HotSpot, Spatial, and Visual Analytics
    1. Chapter 3: Visual Analytics for Crime Analysis and Decision Support
      1. ABSTRACT
      2. INTRODUCTION
      3. VISUAL ANALYTICS
      4. VISUALIZATION TOOLS
      5. VISUALIZATIONS AND HUMAN PERCEPTION
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    2. Chapter 4: Crime Hotspot Detection
      1. ABSTRACT
      2. INTRODUCTION
      3. SCOPE
      4. BACKGROUND
      5. CIRCULAR HOTSPOT DETECTION
      6. RING-SHAPED HOTSPOT DETECTION
      7. LINEAR HOTSPOT DETECTION
      8. CURRENT HOTSPOT DETECTION TOOLS
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. ACKNOWLEDGMENT
      12. REFERENCES
    3. Chapter 5: Visual Data Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. THE VISUAL DATA MINING FRAMEWORK
      5. EXPERIMENTAL SETUP AND RESULTS
      6. RESULTS AND ANALYSIS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
  11. Section 3: Forensics, Suspect Modeling, and Intelligence Gathering
    1. Chapter 6: On the Use of Bayesian Network in Crime Suspect Modelling and Legal Decision Support
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND CONCEPTS AND RELATED STUDY
      4. 3.0 BAYESIAN NETWORKS ADOPTION IN OFFENDER MODELLING
      5. 4.0 COMPLEX SCENARIOS OF BAYESIAN LEARNING AND OFFENDER MODELLING
      6. 5. SUMMARY AND CONCLUDING REMARKS
      7. LIST OF NOTATIONS
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    2. Chapter 7: Forensic Investigation of Digital Crimes in Healthcare Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. SECTION 1: OVERVIEW OF E-HEALTH SYSTEMS
      4. SECTION 2: DIGITAL CRIMES TARGETING HEALTHCARE SYSTEMS
      5. SECTION 3: REQUIREMENTS AND CHALLENGING ISSUES FOR A POSTMORTEM INVESTIGATION OF HEALTHCARE CRIMES
      6. SECTION 4: AUDITING AND INVESTIGATING CRIMES ON E-HEALTH SYSTEMS
      7. SECTION 5: COMPUTER AIDED PHYSICAL CRIMES INVESTIGATION
      8. SECTION 6: ADVANCED ISSUES
      9. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
  12. Section 4: Denial of Service, Cyber-Crime, and Intrusion Detection Management
    1. Chapter 8: Data Mining Analytics for Crime Security Investigation and Intrusion Detection
      1. ABSTRACT
      2. INTRODUCTION
      3. INVESTIGATION OF SECURITY ATTACKS
      4. DATA MINING: PRINCIPLES AND APPLICATIONS
      5. DATA MINING APPROACHES FOR FORENSIC CONDUCTING
      6. DATA MINING TECHNIQUES FOR EVIDENCE COLLECTION
      7. CASES STUDIES
      8. DATA MINING TRENDS FOR ATTACKS USING BIG DATA
      9. REQUIREMENTS FOR EFFICIENT INVESTIGATION BASED ON DATA MINING
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
      15. ENDNOTE
    2. Chapter 9: Automated Identification of Child Abuse in Chat Rooms by Using Data Mining
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. PREPROCESSING METHODS FOR CHAT LOGS
      5. 4. FEATURE EXTRACTION
      6. 5. LEARNING PREDATORY PATTERNS
      7. 6. FUTURE RESEARCH DIRECTIONS
      8. 7. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    3. Chapter 10: Data Mining Techniques for Distributed Denial of Service Attacks Detection in the Internet of Things
      1. ABSTRACT
      2. INTRODUCTION
      3. THE INTERNET OF THINGS
      4. CHARACTERISTICS OF IoT
      5. IoT APPLICATIONS DOMAIN
      6. IoT SECURITY CONCERNS
      7. INTRODUCTION TO DDoS ATTACKS
      8. DDoS ATTACKS BACKGROUND
      9. DDoS DEFENSE MECHANISMS
      10. INTRUSION DETECTION TECHNIQUES (STATE-OF-THE-ART DETECTION)
      11. SUMMARY AND FUTURE RESEARCH DIRECTION
      12. REFERENCES
  13. Conclusion
    1. THOUGHTFUL DISCUSSION ON DATA MINING TRENDS AND APPLICATIONS IN CRIMINAL SCIENCE AND INVESTIGATIONS
  14. Compilation of References
  15. About the Contributors