IEICE TRANSACTIONS on Information
Efficient Masquerade Detection Using SVM Based on Common Command Frequency in Sliding Windows
Summary :
Masqueraders who impersonate other users pose serious threat to computer security. Unfortunately, firewalls or misuse-based intrusion detection systems are generally ineffective in detecting masqueraders. Anomaly detection techniques have been proposed as a complementary approach to overcome such limitations. However, they are not accurate enough in detection, and the rate of false alarm is too high for the technique to be applied in practice. For example, recent empirical studies on masquerade detection using UNIX commands found the accuracy to be below 70%. In this research, we performed a comparative study to investigate the effectiveness of SVM (Support Vector Machine) technique using the same data set and configuration reported in the previous experiments. In order to improve accuracy of masquerade detection, we used command frequencies in sliding windows as feature sets. In addition, we chose to ignore commands commonly used by all the users and introduce the concept of voting engine. Though still imperfect, we were able to improve the accuracy of masquerade detection to 80.1% and 94.8%, whereas previous studies reported accuracy of 69.3% and 62.8% in the same configurations. This study convincingly demonstrates that SVM is useful as an anomaly detection technique and that there are several advantages SVM offers as a tool to detect masqueraders.
- Publication
- IEICE TRANSACTIONS on Information Vol.E87-D No.11 pp.2446-2452
- Publication Date
- 2004/11/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Application Information Security
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Han-Sung KIM, Sung-Deok CHA, "Efficient Masquerade Detection Using SVM Based on Common Command Frequency in Sliding Windows" in IEICE TRANSACTIONS on Information,
vol. E87-D, no. 11, pp. 2446-2452, November 2004, doi: .
Abstract: Masqueraders who impersonate other users pose serious threat to computer security. Unfortunately, firewalls or misuse-based intrusion detection systems are generally ineffective in detecting masqueraders. Anomaly detection techniques have been proposed as a complementary approach to overcome such limitations. However, they are not accurate enough in detection, and the rate of false alarm is too high for the technique to be applied in practice. For example, recent empirical studies on masquerade detection using UNIX commands found the accuracy to be below 70%. In this research, we performed a comparative study to investigate the effectiveness of SVM (Support Vector Machine) technique using the same data set and configuration reported in the previous experiments. In order to improve accuracy of masquerade detection, we used command frequencies in sliding windows as feature sets. In addition, we chose to ignore commands commonly used by all the users and introduce the concept of voting engine. Though still imperfect, we were able to improve the accuracy of masquerade detection to 80.1% and 94.8%, whereas previous studies reported accuracy of 69.3% and 62.8% in the same configurations. This study convincingly demonstrates that SVM is useful as an anomaly detection technique and that there are several advantages SVM offers as a tool to detect masqueraders.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_11_2446/_p
Copy
@ARTICLE{e87-d_11_2446,
author={Han-Sung KIM, Sung-Deok CHA, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Masquerade Detection Using SVM Based on Common Command Frequency in Sliding Windows},
year={2004},
volume={E87-D},
number={11},
pages={2446-2452},
abstract={Masqueraders who impersonate other users pose serious threat to computer security. Unfortunately, firewalls or misuse-based intrusion detection systems are generally ineffective in detecting masqueraders. Anomaly detection techniques have been proposed as a complementary approach to overcome such limitations. However, they are not accurate enough in detection, and the rate of false alarm is too high for the technique to be applied in practice. For example, recent empirical studies on masquerade detection using UNIX commands found the accuracy to be below 70%. In this research, we performed a comparative study to investigate the effectiveness of SVM (Support Vector Machine) technique using the same data set and configuration reported in the previous experiments. In order to improve accuracy of masquerade detection, we used command frequencies in sliding windows as feature sets. In addition, we chose to ignore commands commonly used by all the users and introduce the concept of voting engine. Though still imperfect, we were able to improve the accuracy of masquerade detection to 80.1% and 94.8%, whereas previous studies reported accuracy of 69.3% and 62.8% in the same configurations. This study convincingly demonstrates that SVM is useful as an anomaly detection technique and that there are several advantages SVM offers as a tool to detect masqueraders.},
keywords={},
doi={},
ISSN={},
month={November},}
Copy
TY - JOUR
TI - Efficient Masquerade Detection Using SVM Based on Common Command Frequency in Sliding Windows
T2 - IEICE TRANSACTIONS on Information
SP - 2446
EP - 2452
AU - Han-Sung KIM
AU - Sung-Deok CHA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2004
AB - Masqueraders who impersonate other users pose serious threat to computer security. Unfortunately, firewalls or misuse-based intrusion detection systems are generally ineffective in detecting masqueraders. Anomaly detection techniques have been proposed as a complementary approach to overcome such limitations. However, they are not accurate enough in detection, and the rate of false alarm is too high for the technique to be applied in practice. For example, recent empirical studies on masquerade detection using UNIX commands found the accuracy to be below 70%. In this research, we performed a comparative study to investigate the effectiveness of SVM (Support Vector Machine) technique using the same data set and configuration reported in the previous experiments. In order to improve accuracy of masquerade detection, we used command frequencies in sliding windows as feature sets. In addition, we chose to ignore commands commonly used by all the users and introduce the concept of voting engine. Though still imperfect, we were able to improve the accuracy of masquerade detection to 80.1% and 94.8%, whereas previous studies reported accuracy of 69.3% and 62.8% in the same configurations. This study convincingly demonstrates that SVM is useful as an anomaly detection technique and that there are several advantages SVM offers as a tool to detect masqueraders.
ER -
English
