A Host Based Kernel Level Rootkit Detection Mechanism Using Clustering Technique

  • Jestin Joy
  • Anita John
Part of the Communications in Computer and Information Science book series (CCIS, volume 204)

Abstract

Rootkits are a set of software tools used by an attacker to gain unauthorized access into a system, thereby providing him with privilege to access sensitive data, conceal its own existence and allowing him to install other malicious software. They are difficult to detect due to their elusive nature. Modern rootkit attacks mainly focus on modifying operating system kernel. Existing techniques for detection rely mainly on saving the system state before detection and comparing it with the infected state. Efficient detection is possible by properly differentiating malicious and non malicious activities taking place in a kernel. In this paper we present a novel anomaly detection method for kernel level rootkits based on k-means clustering algorithm.

Keywords

System Call Memory Address Malicious Code Guest Operating System Kernel Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jestin Joy
    • 1
  • Anita John
    • 1
  1. 1.Rajagiri School of Engineering & TechnologyKochiIndia

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