Muutke küpsiste eelistusi

E-raamat: Machine Learning Forensics for Law Enforcement, Security, and Intelligence

2.50/5 (4 hinnangut Goodreads-ist)
(Triangular Marketing, El Paso, Texas, USA)
  • Formaat: 349 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: Taylor & Francis Inc
  • ISBN-13: 9781439860700
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 162,50 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele
Machine Learning Forensics for Law Enforcement, Security, and IntelligenceSuurem pilt
  • Formaat: 349 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: Taylor & Francis Inc
  • ISBN-13: 9781439860700
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Machine learning forensics can be used to recognize patterns of criminal activity, detect network intrusions, and discover evidence. Mena, an artificial intelligence specialist, compiles deductive and inductive tools for analyzing digital evidence in this guide for CIOs, law enforcement personnel, legal and IT professionals, fraud and criminal investigators, and competitive intelligence analysts. The book provides step-by-step guidance on constructing criminal and fraud detection systems for private and governmental organizations, and explains techniques for predicting cyber attacks and human and nonhuman responses based on digital data such as cell phone and email records, browser history, and keyword use. Corporate counterintelligence is also covered. The book is illustrated with b&w screenshots, images, illustrations, and numerous checklists and summary tables. Mena has consulted for the Department of Homeland Security. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)

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.

Introduction ix
The Author xi
Chapter 1 What Is Machine Learning Forensics?
1(36)
1.1 Definition
1(1)
1.2 Digital Maps and Models: Strategies and Technologies
2(1)
1.3 Extractive Forensics: Link Analysis and Text Mining
3(4)
1.4 Inductive Forensics: Clustering Incidents and Crimes
7(3)
1.5 Deductive Forensics: Anticipating Attacks and Precrime
10(11)
1.6 Fraud Detection: On the Web, Wireless, and in Real Time
21(3)
1.7 Cybersecurity Investigations: Self-Organizing and Evolving Analyses
24(4)
1.8 Corporate Counterintelligence: Litigation and Competitive Investigations
28(4)
1.9 A Machine Learning Forensic Worksheet
32(5)
Chapter 2 Digital Investigative Maps and Models: Strategies and Techniques
37(40)
2.1 Forensic Strategies
37(4)
2.2 Decompose the Data
41(1)
2.3 Criminal Data Sets, Reports, and Networks
42(3)
2.4 Real Estate, Auto, and Credit Data Sets
45(1)
2.5 Psychographic and Demographic Data Sets
46(3)
2.6 Internet Data Sets
49(4)
2.7 Deep Packet Inspection (DPI)
53(3)
2.8 Designing a Forensic Framework
56(2)
2.9 Tracking Mechanisms
58(5)
2.10 Assembling Data Streams
63(2)
2.11 Forensic Techniques
65(4)
2.12 Investigative Maps
69(3)
2.13 Investigative Models
72(5)
Chapter 3 Extractive Forensics: Link Analysis and Text Mining
77(48)
3.1 Data Extraction
77(3)
3.2 Link Analysis
80(3)
3.3 Link Analysis Tools
83(13)
3.4 Text Mining
96(2)
3.5 Text Mining Tools
98(25)
3.5.1 Online Text Mining Analytics Tools
99(1)
3.5.2 Commercial Text Mining Analytics Software
99(24)
3.6 From Extraction to Clustering
123(2)
Chapter 4 Inductive Forensics: Clustering Incidents and Crimes
125(34)
4.1 Autonomous Forensics
125(4)
4.2 Self-Organizing Maps
129(3)
4.3 Clustering Software
132(6)
4.3.1 Commercial Clustering Software
132(2)
4.3.2 Free and Open-Source Clustering Software
134(4)
4.4 Mapping Incidents
138(3)
4.5 Clustering Crimes
141(13)
4.6 From Induction to Deduction
154(5)
Chapter 5 Deductive Forensics: Anticipating Attacks and Precrime
159(36)
5.1 Artificial Intelligence and Machine Learning
159(1)
5.2 Decision Trees
160(3)
5.3 Decision Tree Techniques
163(4)
5.4 Rule Generators
167(3)
5.5 Decision Tree Tools
170(14)
5.5.1 Free and Shareware Decision Tree Tools
179(1)
5.5.2 Rule Generator Tools
179(3)
5.5.3 Free Rule Generator Tools
182(2)
5.6 The Streaming Analytical Forensic Processes
184(6)
5.7 Forensic Analysis of Streaming Behaviors
190(1)
5.8 Forensic Real-Time Modeling
191(1)
5.9 Deductive Forensics for Precrime
192(3)
Chapter 6 Fraud Detection: On the Web, Wireless, and in Real Time
195(38)
6.1 Definition and Techniques: Where, Who, and How
195(7)
6.2 The Interviews: The Owners, Victims, and Suspects
202(3)
6.3 The Scene of the Crime: Search for Digital Evidence
205(2)
6.3.1 Four Key Steps in Dealing with Digital Evidence
206(1)
6.4 Searches for Associations: Discovering Links and Text Concepts
207(1)
6.5 Rules of Fraud: Conditions and Clues
208(1)
6.6 A Forensic Investigation Methodology
209(3)
6.6.1 Step One: Understand the Investigation Objective
209(1)
6.6.2 Step Two: Understand the Data
210(1)
6.6.3 Step Three: Data Preparation Strategy
210(1)
6.6.4 Step Four: Forensic Modeling
210(1)
6.6.5 Step Five: Investigation Evaluation
211(1)
6.6.6 Step Six: Detection Deployment
211(1)
6.7 Forensic Ensemble Techniques
212(4)
6.7.1 Stage One: Random Sampling
212(1)
6.7.2 Stage Two: Balance the Data
213(1)
6.7.3 Stage Three: Split the Data
213(1)
6.7.4 Stage Four: Rotate the Data
213(1)
6.7.5 Stage Five: Evaluate Multiple Models
213(1)
6.7.6 Stage Six: Create an Ensemble Model
214(1)
6.7.7 Stage Seven: Measure False Positives and Negatives
215(1)
6.7.8 Stage Eight: Deploy and Monitor
215(1)
6.7.9 Stage Nine: Anomaly Detection
216(1)
6.8 Fraud Detection Forensic Solutions
216(11)
6.9 Assembling an Evolving Fraud Detection Framework
227(6)
Chapter 7 Cybersecurity Investigations: Self-Organizing and Evolving Analyses
233(38)
7.1 What Is Cybersecurity Forensics?
233(1)
7.2 Cybersecurity and Risk
234(2)
7.3 Machine Learning Forensics for Cybersecurity
236(3)
7.4 Deep Packet Inspection (DPI)
239(3)
7.4.1 Layer 7: Application
239(1)
7.4.2 Layer 6: Presentation
240(1)
7.4.3 Layer 5: Session
240(1)
7.4.4 Layer 4: Transport
240(1)
7.4.5 Layer 3: Network
241(1)
7.4.6 Layer 2: Data Link
241(1)
7.4.7 Layer 1: Physical
241(1)
7.4.8 Software Tools Using DPI
241(1)
7.5 Network Security Tools
242(3)
7.6 Combating Phishing
245(2)
7.7 Hostile Code
247(3)
7.8 The Foreign Threat
250(6)
7.8.1 The CNCI Initiative Details
252(4)
7.9 Forensic Investigator Toolkit
256(3)
7.10 Wireless Hacks
259(4)
7.11 Incident Response Check-Off Checklists
263(4)
7.12 Digital Fingerprinting
267(4)
Chapter 8 Corporate Counterintelligence: Litigation and Competitive Investigations
271(36)
8.1 Corporate Counterintelligence
271(3)
8.2 Ratio, Trending, and Anomaly Analyses
274(2)
8.3 E-Mail Investigations
276(7)
8.4 Legal Risk Assessment Audit
283(9)
8.4.2 Inventory of External Inputs to the Process
285(1)
8.4.3 Identify Assets and Threats
286(1)
8.4.4 List Risk Tolerance for Major Events
286(1)
8.4.5 List and Evaluate Existing Protection Mechanisms
287(1)
8.4.6 List and Assess Underprotected Assets and Unaddressed Threats
287(5)
8.5 Competitive Intelligence Investigations
292(10)
8.5 Triangulation Investigations
302(5)
Index 307
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.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97814398607002e.html
Märksõnad: