| | |  | Intrusion Detection | Home » » Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection (Premier Reference Source) | | | | | | | Product Promotions: | | | | | Description: | | Intrusion detection and protection is a key component in the framework of the computer and network security area. Although various classification algorithms and approaches have been developed and proposed over the last decade, the statistically-based method remains the most common approach to anomaly intrusion detection. Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection bridges between applied statistical modeling techniques and network security to provide statistical modeling and simulating approaches to address the needs for intrusion detection and protection. Covering in-depth topics such as network traffic data, anomaly intrusion detection, and prediction events, this authoritative source collects must-read research for network administrators, information and network security professionals, statistics and computer science learners, and researchers in related fields. | | | Product Details: | | | Author:
| Yun Wang | | Hardcover:
| 476 pages | | Publisher:
| IGI Global | | Publication Date:
| October 31, 2008 | | Language:
| English | | ISBN:
| 159904708X | | Product Length:
| 10.2 inches | | Product Width:
| 7.2 inches | | Product Height:
| 1.3 inches | | Product Weight:
| 2.6 pounds | | Package Length:
| 10.24 inches | | Package Width:
| 7.17 inches | | Package Height:
| 1.34 inches | | Package Weight:
| 2.69 pounds | | Average Customer Rating:
|  based on 1 reviews |
| | | | Customer Reviews: | |
Average Customer Review:
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Most Helpful Customer Reviews
Excellent Overview--Mediocre ExecutionJan 06, 2011
By Sojournalist
There aren't that many books that apply the field of statistical analysis to network data, and this is one of the most comprehensive that I have found. The author reviews the current literature and methodology; gives an overview of multiple tools used to conduct data reviews (SAS, Stata, R, and brief mentions of Mathematica and Matlab); provides a basic review of statistics and appropriate probability distributions; and a quick overview of network data characteristics. The meat of the book is the description of algorithms and discussion of applicability of visualization, data reduction, network data modeling for prediction and association, classification, profiling and decision analysis to network data.
The negatives are that there are numerous problems with English usage and grammar, and the index weighs in at a whopping three pages for a 457-page book, making it nearly useless. Additionally, the appendices are found following the chapter contents rather than all together at the end of the book in their usual place.
I'm rating this one at four stars for the thoroughness of the topic coverage. It is an excellent practical introduction to non-statistician network analysts and introduces and demonstrates the algorithmic and software tools used in the analysis of network data--for a variety of purposes including intrusion detection, web site transaction analysis, user behavior analysis, and spam email detection. I personally find it jarring to find wrong word usage, spelling, typographic errors, and other effluvia of poor editorial review, especially in a book this expensive, but I am happy to have all of this information in one volume, and once I know basically where to find exactly what I'm looking for, it will also be a ready reference.
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