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Machine Learning Forensics for Law Enforcement, Security, and Intelligence

Machine Learning Forensics for Law Enforcement, Security, and Intelligence

Machine Learning Forensics for Law Enforcement, Security, and Intelligence
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Machine Learning Forensics for Law Enforcement, Security, and Intelligence Hardback - 2011

by Mena, Jesus

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CRC Press, 2011-06-23. 1. hardcover. Used: Good. 6.10x0.90x9.10. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Machine Learning Forensics for Law Enforcement, Security, and Intelligence
  • Author Mena, Jesus
  • Binding Hardback
  • Edition 1
  • Condition Used: Good
  • Pages 350
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date 2011-06-23
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index, Maps
  • Bookseller's Inventory # SONG1439860696
  • ISBN 9781439860694 / 1439860696
  • Weight 1.35 lbs (0.61 kg)
  • Dimensions 9.1 x 6.1 x 0.9 in (23.11 x 15.49 x 2.29 cm)
  • Size 6.10x0.90x9.10
  • Themes
    • Aspects (Academic): Crime/Criminology
  • Category Legal Reference / Law Profession
  • Library of Congress subjects Electronic evidence, Computer security
  • Library of Congress Catalogue Number 2011026877
  • Dewey Decimal Code 363.250
  • Quantity available 1

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From the publisher

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.

Media reviews

Citations

  • Reference and Research Bk News, 10/01/2011, Page 114

About the author

Jess 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.

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