Although the use of data mining for security and malware
detection is quickly on the rise, most books on the subject provide
high-level theoretical discussions to the near exclusion of the
practical aspects. Breaking the mold, Data Mining Tools for Malware
Detection provides a step-by-step breakdown of how to develop data
mining tools for malware detection. Integrating theory with
practical techniques and experimental results, it focuses on
malware detection applications for email worms, malicious code,
remote exploits, and botnets.
The authors describe the systems they have designed and
developed: email worm detection using data mining, a scalable
multi-level feature extraction technique to detect malicious
executables, detecting remote exploits using data mining, and
flow-based identification of botnet traffic by mining multiple log
files. For each of these tools, they detail the system
architecture, algorithms, performance results, and limitations.
- Discusses data mining for emerging applications, including
adaptable malware detection, insider threat detection, firewall
policy analysis, and real-time data mining
- Includes four appendices that provide a firm foundation in data
management, secure systems, and the semantic web
- Describes the authors tools for stream data mining
From algorithms to experimental results, this is one of the few
books that will be equally valuable to those in industry,
government, and academia. It will help technologists decide which
tools to select for specific applications, managers will learn how
to determine whether or not to proceed with a data mining project,
and developers will find innovative alternative designs for a range
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