TY - GEN
T1 - Rank-Based Univariate Selection for Intrusion Detection System
AU - Safitri, Winda Ayu
AU - Ahmad, Tohari
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Intrusion Detection System (IDS) is a scheme, which supervises network traffic and monitors suspicious activities in a network system. Nowadays, a potential solution to efficiently detect network intrusions is to use a machine learning (ML)based IDS system. There are numerous issues with IDS, mainly in the dataset for the training. One of the problems that often arises is increasing detection accuracy and minimizing computation time in training the data. There is a suitable dataset for detecting various intrusions, which is the NSL-KDD. In this dataset, there is a number of features that are redundant and irrelevant to access. We suggest a strategy in this study to increase IDS performance by combining univariate selection and Support Vector Machine (SVM) for classification. It is ideal for categorization in IDS because it has high performance. Data reduction is used to increase the accuracy and decrease computation time. The result of experiments shows that the proposed method effectively improves the accuracy.
AB - Intrusion Detection System (IDS) is a scheme, which supervises network traffic and monitors suspicious activities in a network system. Nowadays, a potential solution to efficiently detect network intrusions is to use a machine learning (ML)based IDS system. There are numerous issues with IDS, mainly in the dataset for the training. One of the problems that often arises is increasing detection accuracy and minimizing computation time in training the data. There is a suitable dataset for detecting various intrusions, which is the NSL-KDD. In this dataset, there is a number of features that are redundant and irrelevant to access. We suggest a strategy in this study to increase IDS performance by combining univariate selection and Support Vector Machine (SVM) for classification. It is ideal for categorization in IDS because it has high performance. Data reduction is used to increase the accuracy and decrease computation time. The result of experiments shows that the proposed method effectively improves the accuracy.
KW - Data reduction
KW - Intrusion detection system
KW - Local outlier factor
KW - Network infrastructure
KW - Univariate selection
UR - http://www.scopus.com/inward/record.url?scp=85125927312&partnerID=8YFLogxK
U2 - 10.1109/ICOIACT53268.2021.9563981
DO - 10.1109/ICOIACT53268.2021.9563981
M3 - Conference contribution
AN - SCOPUS:85125927312
T3 - ICOIACT 2021 - 4th International Conference on Information and Communications Technology: The Role of AI in Health and Social Revolution in Turbulence Era
SP - 164
EP - 168
BT - ICOIACT 2021 - 4th International Conference on Information and Communications Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Information and Communications Technology, ICOIACT 2021
Y2 - 30 August 2021
ER -