Data Mining Tools for Malware Detection

Mehedy Masud, Latifur Khan, Bhavani Thuraisingham

December 7, 2011 by Auerbach Publications
Reference - 450 Pages - 131 B/W Illustrations
ISBN 9781439854549 - CAT# K12530

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  • Details systems for detecting email worms, malicious executables, remote exploits, and botnets
  • Considers performance results, unique contributions, and limitations of each system
  • Covers algorithms as well as the practical aspects
  • Reports on experimental results


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 of applications.