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Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

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@article{IJASEIT3421,
   author = {Muhammad Ahsan and Muhammad Mashuri and Heri Kuswanto and Dedy Dwi Prastyo},
   title = {Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {8},
   number = {5},
   year = {2018},
   pages = {1905--1911},
   keywords = {intrusion detection; multivariate control chart; hotelling’s T2; PCA},
   abstract = {

Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling’s T2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by a large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T2 based on PCA for IDS. The KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T2 based PCA will be compared with conventional T2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling’s T2 based on PCA has excellent performance to detect an anomaly in the network. Compared to conventional T2 control chart, the T2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time. 

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3421},    doi = {10.18517/ijaseit.8.5.3421} }

EndNote

%A Ahsan, Muhammad
%A Mashuri, Muhammad
%A Kuswanto, Heri
%A Prastyo, Dedy Dwi
%D 2018
%T Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA
%B 2018
%9 intrusion detection; multivariate control chart; hotelling’s T2; PCA
%! Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA
%K intrusion detection; multivariate control chart; hotelling’s T2; PCA
%X 

Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling’s T2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by a large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T2 based on PCA for IDS. The KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T2 based PCA will be compared with conventional T2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling’s T2 based on PCA has excellent performance to detect an anomaly in the network. Compared to conventional T2 control chart, the T2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time. 

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3421 %R doi:10.18517/ijaseit.8.5.3421 %J International Journal on Advanced Science, Engineering and Information Technology %V 8 %N 5 %@ 2088-5334

IEEE

Muhammad Ahsan,Muhammad Mashuri,Heri Kuswanto and Dedy Dwi Prastyo,"Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 5, pp. 1905-1911, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.5.3421.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ahsan, Muhammad
AU  - Mashuri, Muhammad
AU  - Kuswanto, Heri
AU  - Prastyo, Dedy Dwi
PY  - 2018
TI  - Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 5
Y2  - 2018
SP  - 1905
EP  - 1911
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - intrusion detection; multivariate control chart; hotelling’s T2; PCA
N2  - 

Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling’s T2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by a large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T2 based on PCA for IDS. The KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T2 based PCA will be compared with conventional T2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling’s T2 based on PCA has excellent performance to detect an anomaly in the network. Compared to conventional T2 control chart, the T2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time. 

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3421 DO - 10.18517/ijaseit.8.5.3421

RefWorks

RT Journal Article
ID 3421
A1 Ahsan, Muhammad
A1 Mashuri, Muhammad
A1 Kuswanto, Heri
A1 Prastyo, Dedy Dwi
T1 Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 5
YR 2018
SP 1905
OP 1911
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 intrusion detection; multivariate control chart; hotelling’s T2; PCA
AB 

Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling’s T2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by a large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T2 based on PCA for IDS. The KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T2 based PCA will be compared with conventional T2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling’s T2 based on PCA has excellent performance to detect an anomaly in the network. Compared to conventional T2 control chart, the T2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time. 

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3421 DO - 10.18517/ijaseit.8.5.3421