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An LDoS attack detection method based on FSWT time-frequency distribution
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AI
DOI：10.1016/j.eswa.2024.125006.png)
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Abstract
En 中文
The Low-rate Denial-of-Service (LDoS) attack is a stealthy and periodic attack, which belongs to the category of DoS attacks. The LDoS attack maliciously preempts and consumes target resources, causing the targeted network performance to decline and affecting the service quality. The LDoS attack is highly destructive and difficult to detect and defend against due to its abnormal attack behavior. In this paper, we discuss the network traffic behavior in the time domain, frequency domain, and time-frequency domain, and we find that the time- frequency domain contains more detailed information than the time domain and frequency domain alone. Considering the limitations of the existing time-frequency domain transformation methods, an LDoS attack detection method based on Frequency Slice Wavelet Transformation (FSWT) is presented in this paper. The entropy, ratio of energy, contrast, and correlation are extracted from the time-frequency distribution to depict the network traffic, and a decision tree is trained to detect the LDoS attack in this paper. According to the experimental results on NS2, testbed, and the comparative experiment, we conclude that the method presented in this paper performs well.
Keywords:
LDoS attack
Time-frequency domain
FSWT
Time-frequency distribution
Decision tree
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W