The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
악성 네트워크 트래픽을 판별하기 위한 효율적인 탐지 메커니즘의 개발은 최근 몇 년간 네트워크 보안 분야의 중요한 연구 주제였습니다. 본 연구에서는 기계학습 알고리즘을 기반으로 침입탐지시스템(IDS)을 구현하여 주기적으로 변환하고 분석한다. 현실 거의 실시간으로 캠퍼스 환경의 네트워크 트래픽을 모니터링합니다. 본 연구의 초점은 IDS의 탐지율을 향상시키는 방법과 전통적인 규칙 기반 시스템에서 벗어나 잘 알려지지 않은 포트 공격을 더 많이 탐지하는 방법을 찾는 것입니다. 판별 정확도를 높이기 위해 4가지 새로운 기능이 사용됩니다. 또한, 데이터 세트의 균형을 맞추는 알고리즘을 사용하여 학습 데이터 세트를 구성했으며, 이를 통해 학습 모델이 실제 환경의 상황을 보다 정확하게 반영할 수 있습니다.
Cheng-Chung KUO
National Cheng Kung University
Ding-Kai TSENG
National Cheng Kung University
Chun-Wei TSAI
National Sun Yat Sen University
Chu-Sing YANG
National Cheng Kung University
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Cheng-Chung KUO, Ding-Kai TSENG, Chun-Wei TSAI, Chu-Sing YANG, "An Effective Feature Extraction Mechanism for Intrusion Detection System" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 11, pp. 1814-1827, November 2021, doi: 10.1587/transinf.2021NGP0007.
Abstract: The development of an efficient detection mechanism to determine malicious network traffic has been a critical research topic in the field of network security in recent years. This study implemented an intrusion-detection system (IDS) based on a machine learning algorithm to periodically convert and analyze real network traffic in the campus environment in almost real time. The focuses of this study are on determining how to improve the detection rate of an IDS and how to detect more non-well-known port attacks apart from the traditional rule-based system. Four new features are used to increase the discriminant accuracy. In addition, an algorithm for balancing the data set was used to construct the training data set, which can also enable the learning model to more accurately reflect situations in real environment.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021NGP0007/_p
부
@ARTICLE{e104-d_11_1814,
author={Cheng-Chung KUO, Ding-Kai TSENG, Chun-Wei TSAI, Chu-Sing YANG, },
journal={IEICE TRANSACTIONS on Information},
title={An Effective Feature Extraction Mechanism for Intrusion Detection System},
year={2021},
volume={E104-D},
number={11},
pages={1814-1827},
abstract={The development of an efficient detection mechanism to determine malicious network traffic has been a critical research topic in the field of network security in recent years. This study implemented an intrusion-detection system (IDS) based on a machine learning algorithm to periodically convert and analyze real network traffic in the campus environment in almost real time. The focuses of this study are on determining how to improve the detection rate of an IDS and how to detect more non-well-known port attacks apart from the traditional rule-based system. Four new features are used to increase the discriminant accuracy. In addition, an algorithm for balancing the data set was used to construct the training data set, which can also enable the learning model to more accurately reflect situations in real environment.},
keywords={},
doi={10.1587/transinf.2021NGP0007},
ISSN={1745-1361},
month={November},}
부
TY - JOUR
TI - An Effective Feature Extraction Mechanism for Intrusion Detection System
T2 - IEICE TRANSACTIONS on Information
SP - 1814
EP - 1827
AU - Cheng-Chung KUO
AU - Ding-Kai TSENG
AU - Chun-Wei TSAI
AU - Chu-Sing YANG
PY - 2021
DO - 10.1587/transinf.2021NGP0007
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2021
AB - The development of an efficient detection mechanism to determine malicious network traffic has been a critical research topic in the field of network security in recent years. This study implemented an intrusion-detection system (IDS) based on a machine learning algorithm to periodically convert and analyze real network traffic in the campus environment in almost real time. The focuses of this study are on determining how to improve the detection rate of an IDS and how to detect more non-well-known port attacks apart from the traditional rule-based system. Four new features are used to increase the discriminant accuracy. In addition, an algorithm for balancing the data set was used to construct the training data set, which can also enable the learning model to more accurately reflect situations in real environment.
ER -