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Research on DoS Attacks Intrusion Detection Model Based on Multi-Dimensional Space Feature Vector Expansion K-Means Algorithm

Lijun GAO, Zhenyi BIAN, Maode MA

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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