Rating:
(10 reviews)
Author: Jesus Mena
ISBN : 0750676132
New from $45.00
Format: PDF, EPUB
Download for free books Free Investigative Data Mining for Security and Criminal Detection [Paperback] for everyone book mediafire, rapishare, and mirror link
Investigative Data Mining for Security and Criminal Detection is the first book to outline how data mining technologies can be used to combat crime in the 21st century. It introduces security managers, law enforcement investigators, counter-intelligence agents, fraud specialists, and information security analysts to the latest data mining techniques and shows how they can be used as investigative tools. Readers will learn how to search public and private databases and networks to flag potential security threats and root out criminal activities even before they occur.
The groundbreaking book reviews the latest data mining technologies including intelligent agents, link analysis, text mining, decision trees, self-organizing maps, machine learning, and neural networks. Using clear, understandable language, it explains the application of these technologies in such areas as computer and network security, fraud prevention, law enforcement, and national defense. International case studies throughout the book further illustrate how these technologies can be used to aid in crime prevention.
Investigative Data Mining for Security and Criminal Detection will also serve as an indispensable resource for software developers and vendors as they design new products for the law enforcement and intelligence communities.
Key Features:
* Covers cutting-edge data mining technologies available to use in evidence gathering and collection
* Includes numerous case studies, diagrams, and screen captures to illustrate real-world applications of data mining
* Easy-to-read format illustrates current and future data mining uses in preventative law enforcement, criminal profiling, counter-terrorist initiatives, and forensic science
* Introduces cutting-edge technologies in evidence gathering and collection, using clear non-technical language
* Illustrates current and future applications of data mining tools in preventative law enforcement, homeland security, and other areas of crime detection and prevention
* Shows how to construct predictive models for detecting criminal activity and for behavioral profiling of perpetrators
* Features numerous Web links, vendor resources, case studies, and screen captures illustrating the use of artificial intelligence (AI) technologies
Books with free ebook downloads available Free Investigative Data Mining for Security and Criminal Detection
- Paperback: 272 pages
- Publisher: Butterworth-Heinemann; 1 edition (December 30, 2002)
- Language: English
- ISBN-10: 0750676132
- ISBN-13: 978-0750676137
- Product Dimensions: 0.8 x 6.9 x 9.1 inches
- Shipping Weight: 1.6 pounds (View shipping rates and policies)
Free Investigative Data Mining for Security and Criminal Detection
I read "Investigative Data Mining for Security and Criminal Detection" (IDM) after attending the 2003 Recent Advances in Intrusion Detection (RAID) conference. Researchers at RAID mentioned "self-organizing maps," "neural networks," "machine learning," and other unfamiliar topics. Mena's book helped me understand these subjects in the context of performing data mining. If you steer clear of the author's discussion of intrusion detection in chapter 10, you'll find IDM enlightening and a little scary.
Author Jesus Mena defines investigative data mining as "the visualization, organization, sorting, clustering, segmenting, and predicting of criminal behavior" (p.1). His book strays from this definition, as he also covers simply discovering patterns of activity for responding to events. Accomplishing this task requires investigative data warehousing, link analysis, software agents, text mining, neural networks, and machine learning. Mena addresses each technique in its own chapter, offering descriptions, case studies, and tools. Two types of data mining analysis exist: descriptive, such as a chart, graph, or decision tree; and predictive, obtained via neural networks and machine learning (p.261). Mena also describes mining via "top-down" vs "bottom-up" approaches. The first involves an analyst exploring data to support his theories. The second relies on software to find patterns in data not imagined by a human analyst (p.343).
Mena is most effective when he writes about what he knows best. I loved chapter 9, where he explains cell phone, insurance, and financial frauds. Much of what he wrote applied directly to my interest in network security monitoring and intrusion detection. Chapter 10 (Intrusion Detection), however, is best ignored.
-
Download Link 2