IEICE TRANSACTIONS on Communications
Research on DoS Attacks Intrusion Detection Model Based on Multi-Dimensional Space Feature Vector Expansion K-Means Algorithm
Summary :
DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In recent years, K-Means and improved variants have been widely examined for security intrusion detection, but the detection accuracy to data is not satisfactory. In this paper we propose a multi-dimensional space feature vector expansion K-Means model to detect threats in the network environment. The model uses a genetic algorithm to optimize the weight of K-Means multi-dimensional space feature vector, which greatly improves the detection rate against 6 typical Dos attacks. Furthermore, in order to verify the correctness of the model, this paper conducts a simulation on the NSL-KDD data set. The results show that the algorithm of multi-dimensional space feature vectors expansion K-Means improves the recognition accuracy to 96.88%. Furthermore, 41 kinds of feature vectors in NSL-KDD are analyzed in detail according to a large number of experimental training. The feature vector of the probability positive return of security attack detection is accurately extracted, and a comparison chart is formed to support subsequent research. A theoretical analysis and experimental results show that the multi-dimensional space feature vector expansion K-Means algorithm has a good application in the detection of DDos attacks.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E104-B No.11 pp.1377-1385
- Publication Date
- 2021/11/01
- Publicized
- 2021/04/22
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2020EBP3192
- Type of Manuscript
- PAPER
- Category
- Fundamental Theories for Communications
Authors
Lijun GAO
Shenyang Aerospace University
Zhenyi BIAN
Shenyang Aerospace University
Maode MA
Nanyang Technological University
Keyword
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Lijun GAO, Zhenyi BIAN, Maode MA, "Research on DoS Attacks Intrusion Detection Model Based on Multi-Dimensional Space Feature Vector Expansion K-Means Algorithm" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 11, pp. 1377-1385, November 2021, doi: 10.1587/transcom.2020EBP3192.
Abstract: DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In recent years, K-Means and improved variants have been widely examined for security intrusion detection, but the detection accuracy to data is not satisfactory. In this paper we propose a multi-dimensional space feature vector expansion K-Means model to detect threats in the network environment. The model uses a genetic algorithm to optimize the weight of K-Means multi-dimensional space feature vector, which greatly improves the detection rate against 6 typical Dos attacks. Furthermore, in order to verify the correctness of the model, this paper conducts a simulation on the NSL-KDD data set. The results show that the algorithm of multi-dimensional space feature vectors expansion K-Means improves the recognition accuracy to 96.88%. Furthermore, 41 kinds of feature vectors in NSL-KDD are analyzed in detail according to a large number of experimental training. The feature vector of the probability positive return of security attack detection is accurately extracted, and a comparison chart is formed to support subsequent research. A theoretical analysis and experimental results show that the multi-dimensional space feature vector expansion K-Means algorithm has a good application in the detection of DDos attacks.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020EBP3192/_p
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@ARTICLE{e104-b_11_1377,
author={Lijun GAO, Zhenyi BIAN, Maode MA, },
journal={IEICE TRANSACTIONS on Communications},
title={Research on DoS Attacks Intrusion Detection Model Based on Multi-Dimensional Space Feature Vector Expansion K-Means Algorithm},
year={2021},
volume={E104-B},
number={11},
pages={1377-1385},
abstract={DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In recent years, K-Means and improved variants have been widely examined for security intrusion detection, but the detection accuracy to data is not satisfactory. In this paper we propose a multi-dimensional space feature vector expansion K-Means model to detect threats in the network environment. The model uses a genetic algorithm to optimize the weight of K-Means multi-dimensional space feature vector, which greatly improves the detection rate against 6 typical Dos attacks. Furthermore, in order to verify the correctness of the model, this paper conducts a simulation on the NSL-KDD data set. The results show that the algorithm of multi-dimensional space feature vectors expansion K-Means improves the recognition accuracy to 96.88%. Furthermore, 41 kinds of feature vectors in NSL-KDD are analyzed in detail according to a large number of experimental training. The feature vector of the probability positive return of security attack detection is accurately extracted, and a comparison chart is formed to support subsequent research. A theoretical analysis and experimental results show that the multi-dimensional space feature vector expansion K-Means algorithm has a good application in the detection of DDos attacks.},
keywords={},
doi={10.1587/transcom.2020EBP3192},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - Research on DoS Attacks Intrusion Detection Model Based on Multi-Dimensional Space Feature Vector Expansion K-Means Algorithm
T2 - IEICE TRANSACTIONS on Communications
SP - 1377
EP - 1385
AU - Lijun GAO
AU - Zhenyi BIAN
AU - Maode MA
PY - 2021
DO - 10.1587/transcom.2020EBP3192
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2021
AB - DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In recent years, K-Means and improved variants have been widely examined for security intrusion detection, but the detection accuracy to data is not satisfactory. In this paper we propose a multi-dimensional space feature vector expansion K-Means model to detect threats in the network environment. The model uses a genetic algorithm to optimize the weight of K-Means multi-dimensional space feature vector, which greatly improves the detection rate against 6 typical Dos attacks. Furthermore, in order to verify the correctness of the model, this paper conducts a simulation on the NSL-KDD data set. The results show that the algorithm of multi-dimensional space feature vectors expansion K-Means improves the recognition accuracy to 96.88%. Furthermore, 41 kinds of feature vectors in NSL-KDD are analyzed in detail according to a large number of experimental training. The feature vector of the probability positive return of security attack detection is accurately extracted, and a comparison chart is formed to support subsequent research. A theoretical analysis and experimental results show that the multi-dimensional space feature vector expansion K-Means algorithm has a good application in the detection of DDos attacks.
ER -
English
