  For the traditional denial-of-service attack detection methods have complex
algorithms and high computational overhead, which are difficult to meet the
demand of online detection; and the experimental environment is mostly a
simulation platform, which is difficult to deploy in real network environment,
we propose a real network environment-oriented LDoS attack detection method
based on the time-frequency characteristics of traffic data. All the traffic
data flowing through the Web server is obtained through the acquisition storage
system, and the detection data set is constructed using pre-processing; the
simple features of the flow fragments are used as input, and the deep neural
network is used to learn the time-frequency domain features of normal traffic
features and generate reconstructed sequences, and the LDoS attack is
discriminated based on the differences between the reconstructed sequences and
the input data in the time-frequency domain. The experimental results show that
the proposed method can accurately detect the attack features in the flow
fragments in a very short time and achieve high detection accuracy for complex
and diverse LDoS attacks; since only the statistical features of the packets
are used, there is no need to parse the packet data, which can be adapted to
different network environments.
