Machine learning forensics for law enforcement, security, and intelligence

 Content: What Is Machine Learning Forensics?  Definition  Digital Maps and Models: Strategies and Technologies  Extractive Forensics: Link Analysis and Text Mining  Inductive Forensics: Clustering Incidents and Crimes  Deductive Forensics: Anticipating Attacks and Precrime  Fraud Detection: On the Web, Wireless, and in Real Time  Cybersecurity Investigations: Self-Organizing and Evolving Analyses  Corporate Counterintelligence: Litigation and Competitive Investigations  A Machine Learning Forensic Worksheet  Digital Investigative Maps and Models: Strategies and Techniques  Forensic Strategies  Decompose the Data  Criminal Data Sets, Reports, and Networks  Real Estate, Auto, and Credit Data Sets  Psychographic and Demographic Data Sets  Internet Data Sets  Deep Packet Inspection (DPI)  Designing a Forensic Framework  Tracking Mechanisms  Assembling Data Streams  Forensic Techniques  Investigative Maps  Investigative Models  Extractive Forensics: Link Analysis and Text Mining  Data Extraction  Link Analysis  Link Analysis Tools  Text Mining  Text Mining Tools  Online Text Mining Analytics Tools  Commercial Text Mining Analytics Software  From Extraction to Clustering  Inductive Forensics: Clustering Incidents and Crimes  Autonomous Forensics  Self-Organizing Maps  Clustering Software  Commercial Clustering Software  Free and Open-Source Clustering Software  Mapping Incidents  Clustering Crimes  From Induction to Deduction  Deductive Forensics: Anticipating Attacks and Precrime  Artificial Intelligence and Machine Learning  Decision Trees  Decision Tree Techniques  Rule Generators  Decision Tree Tools  Free and Shareware Decision Tree Tools  Rule Generator Tools  Free Rule Generator Tools  The Streaming Analytical Forensic Processes  Forensic Analysis of Streaming Behaviors  Forensic Real-Time Modeling  Deductive Forensics for Precrime  Fraud Detection: On the Web, Wireless, and in Real Time Definition and Techniques: Where, Who, and How  The Interviews: The Owners, Victims, and Suspects  The Scene of the Crime: Search for Digital Evidence  Four Key Steps in Dealing with Digital Evidence  Searches for Associations: Discovering Links and Text Concepts  Rules of Fraud: Conditions and Clues  A Forensic Investigation Methodology  Step One: Understand the Investigation Objective  Step Two: Understand the Data  Step Three: Data Preparation Strategy  Step Four: Forensic Modeling  Step Five: Investigation Evaluation  Step Six: Detection Deployment  Forensic Ensemble Techniques  Stage One: Random Sampling  Stage Two: Balance the Data  Stage Three: Split the Data  Stage Four: Rotate the Data  Stage Five: Evaluate Multiple Models  Stage Six: Create an Ensemble Model  Stage Seven: Measure False Positives and Negatives  Stage Eight: Deploy and Monitor  Stage Nine: Anomaly Detection  Fraud Detection Forensic Solutions  Assembling an Evolving Fraud Detection Framework  Cybersecurity Investigations: Self - Organizing and Evolving Analyses  What Is Cybersecurity Forensics?  Cybersecurity and Risk  Machine Learning Forensics for Cybersecurity  Deep Packet Inspection (DPI)  Layer 7: Application  Layer 6: Presentation  Layer 5: Session  Layer 4: Transport  Layer 3: Network  Layer 2: Data Link  Layer 1: Physical  Software Tools Using DPI  Network Security Tools  Combating Phishing  Hostile Code  The Foreign Threat  The CNCI Initiative Details  Forensic Investigator Toolkit  Wireless Hacks  Incident Response Check-Off Checklists  Digital Fingerprint  Corporate Counterintelligence: Litigation and Competitive Investigations Corporate Counterintelligence  Ratio, Trending, and Anomaly Analyses  E-Mail Investigations  Legal Risk Assessment Audit  Inventory of External Inputs to the Process  Identify Assets and Threats  List Risk Tolerance for Major Events  List and Evaluate Existing Protection Mechanisms  List and Assess Underprotected Assets and Unaddressed Threats  Competitive Intelligence Investigations  Triangulation Investigations  Index