Machine Learning Forensics for Law Enforcement, Security, and Intelligence  book cover
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1st Edition

Machine Learning Forensics for Law Enforcement, Security, and Intelligence




ISBN 9781439860694
Published June 23, 2011 by Auerbach Publications
349 Pages - 60 B/W Illustrations

 
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Book Description

Increasingly, crimes and fraud are digital in nature, occurring at breakneck speed and encompassing large volumes of data. To combat this unlawful activity, knowledge about the use of machine learning technology and software is critical. Machine Learning Forensics for Law Enforcement, Security, and Intelligence integrates an assortment of deductive and instructive tools, techniques, and technologies to arm professionals with the tools they need to be prepared and stay ahead of the game.

Step-by-step instructions

The book is a practical guide on how to conduct forensic investigations using self-organizing clustering map (SOM) neural networks, text extraction, and rule generating software to "interrogate the evidence." This powerful data is indispensable for fraud detection, cybersecurity, competitive counterintelligence, and corporate and litigation investigations. The book also provides step-by-step instructions on how to construct adaptive criminal and fraud detection systems for organizations.

Prediction is the key

Internet activity, email, and wireless communications can be captured, modeled, and deployed in order to anticipate potential cyber attacks and other types of crimes. The successful prediction of human reactions and server actions by quantifying their behaviors is invaluable for pre-empting criminal activity. This volume assists chief information officers, law enforcement personnel, legal and IT professionals, investigators, and competitive intelligence analysts in the strategic planning needed to recognize the patterns of criminal activities in order to predict when and where crimes and intrusions are likely to take place.

Table of Contents

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

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Author(s)

Biography

Jesús Mena is a former Internal Revenue Service Artificial Intelligence specialist and the author of numerous data mining, web analytics, law enforcement, homeland security, forensic, and marketing books. Mena has also written dozens of articles and consulted with several businesses and governmental agencies. He has over 20 years’ experience in expert systems, rule induction, decision trees, neural networks, self-organizing maps, regression, visualization, and machine learning and has worked on data mining projects involving clustering, segmentation, classification, profiling and personalization with government, web, retail, insurance, credit card, financial and healthcare data sets. He has worked, written, and lectured on various behavioral analytics and social networking techniques, personalization mechanisms, web and mobile networks, real-time psychographics, tracking and profiling engines, log analyzing tools, packet sniffers, voice and text recognition software, geolocation and behavioral targeting systems, real-time streaming analytical software, ensemble techniques, and digital fingerprinting.