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	• Investigative Data Mining for Security and Criminal Detection, First Edition Jesus Mena Butterworth-Heinemann, 2003 - 272 pages average customer review:based on 8 reviews view larger image
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highly recommended

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Of the most interest to me was the intrusion detection chapter, but in addition to a well-known facts on IDS technology it provided few details on how exactly data mining helps. MITRE case study seems to mostly hint at things rather then show how they were done in this project. I did pick up some ideas from it.

Anton Chuvakin, Ph.D., GCIA, GCIH is a Senior Security Analyst with a major information security company. His areas of infosec expertise include intrusion detection, UNIX security, forensics, honeypots, etc. In his spare time, he maintains his security portal info-secure.org

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When on-message, an excellent intro to data mining

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. Mena does not appear to understand computer security, and neither do his editors. He calls Snort a "freeware site-based system IDS," in contrast with "network-based IDSs such as RealSecure" (p.306). He labels tcpdump an "attack" tool and says "this is utility for eavesdropping for passwords" (his typos) (p.307) and describes "rhosts" in a "stealth" attack phase as "this utility will evaluate hosts and lists hosts and users who are trusted by the local host" (p.308). Mena isn't a "security guy," either; he lumps "threats and vulnerabilities" together as "weaknesses or flaws in a system, such as a hole in security or a back door" (p.14). A threat is one or more entities with capabilities and intentions sufficient to exploit vulnerabilities in information resources, while a vulnerability is a weakness in design, configuration, or deployment which allow threats to abuse, subvert, or break information resources.

Overall, I really enjoyed IDM. Mena makes numerous fascinating insights. While his prose is somewhat repetitive, he explains the key points needed to get data mining newbies up to speed. In light of the recent revelations of jetBlue sharing data with the government, the techniques Mena describes are both powerful and disturbing.

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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

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