Title :
Taxonomy of statistical based anomaly detection techniques for intrusion detection
Author :
Qayyum, A. ; Islam, M.H. ; Jamil, M.
Abstract :
Security threats to the computer systems have raised the importance of intrusion detection systems. With the advent of new vulnerabilities to computer systems new techniques for intrusion detection have been implemented. Statistical based anomaly detection techniques use statistical properties and statistical tests to determine whether "observed behavior" deviate significantly from the "expected behavior". Statistical based anomaly detection has been a wide area of interest for researchers since it provides the base line for developing a promising technique. This paper presents a guideline for statistical based anomaly detection techniques with the perspective of various scenarios and areas of implementation.
Keywords :
computer networks; security of data; statistical analysis; computer systems; intrusion detection; intrusion detection systems; statistical based anomaly detection technique taxonomy; statistical properties; Computer aided software engineering; Computer crime; Computer networks; Computer security; Computer viruses; Guidelines; Intrusion detection; Statistical analysis; Taxonomy; Testing;
Conference_Titel :
Emerging Technologies, 2005. Proceedings of the IEEE Symposium on
Print_ISBN :
0-7803-9247-7
DOI :
10.1109/ICET.2005.1558893