Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems

Jecheva, Veselina, and Nikolova, Evgeniya. "Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems." Serdica Journal of Computing 3.4 (2009): 335-358. <http://eudml.org/doc/11365>.

@article{Jecheva2009,
abstract = {Intrusion detection is a critical component of security information systems. The intrusion detection process attempts to detect malicious attacks by examining various data collected during processes on the protected system. This paper examines the anomaly-based intrusion detection based on sequences of system calls. The point is to construct a model that describes normal or acceptable system activity using the classification trees approach. The created database is utilized as a basis for distinguishing the intrusive activity from the legal one using string metric algorithms. The major results of the implemented simulation experiments are presented and discussed as well.},
author = {Jecheva, Veselina, Nikolova, Evgeniya},
journal = {Serdica Journal of Computing},
keywords = {Intrusion Detection; Data Mining; String Metrics; Similarity Coefficients; intrusion detection; data mining; string metrics; similarity coefficients},
language = {eng},
number = {4},
pages = {335-358},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems},
url = {http://eudml.org/doc/11365},
volume = {3},
year = {2009},
}

TY - JOUR
AU - Jecheva, Veselina
AU - Nikolova, Evgeniya
TI - Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems
JO - Serdica Journal of Computing
PY - 2009
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 3
IS - 4
SP - 335
EP - 358
AB - Intrusion detection is a critical component of security information systems. The intrusion detection process attempts to detect malicious attacks by examining various data collected during processes on the protected system. This paper examines the anomaly-based intrusion detection based on sequences of system calls. The point is to construct a model that describes normal or acceptable system activity using the classification trees approach. The created database is utilized as a basis for distinguishing the intrusive activity from the legal one using string metric algorithms. The major results of the implemented simulation experiments are presented and discussed as well.
LA - eng
KW - Intrusion Detection; Data Mining; String Metrics; Similarity Coefficients; intrusion detection; data mining; string metrics; similarity coefficients
UR - http://eudml.org/doc/11365
ER -