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Research on the Impact of Combining Big Data and Deep Learning Technology on Network Security Information Security Protection

Xingmeng Wang

Xingmeng Wang

School of Information, Shanxi University of Finance and EconomicsTaiyuan, China
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Wang, Xingmeng
  
19 mar 2025
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Pubblicato online: 19 mar 2025
Ricevuto: 14 nov 2024
Accettato: 12 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0439
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© 2025 Xingmeng Wang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Flowchart of the intrusion detection system
Flowchart of the intrusion detection system

Figure 2.

Intrusion detection process based on deep learning
Intrusion detection process based on deep learning

Figure 3.

LSTM network unit structure
LSTM network unit structure

Figure 4.

Framework for training GAN-LSTM model
Framework for training GAN-LSTM model

Figure 5.

The relationship between the loss function and the number of iterations
The relationship between the loss function and the number of iterations

Figure 6.

The time domain of pwnloris and the activation diagram of Conv 6
The time domain of pwnloris and the activation diagram of Conv 6

Figure 7.

Slow post’s time domain and Conv 6 activation diagram
Slow post’s time domain and Conv 6 activation diagram

Figure 8.

The time span is the network service data flow distribution for two weeks
The time span is the network service data flow distribution for two weeks

Figure 9.

The periodic sequence of decomposition
The periodic sequence of decomposition

Figure 10.

Cyclic trend clustering results of network information
Cyclic trend clustering results of network information

Test results for three LDoS attacks that support HTTPS encryption

Attack type Accuracy (%) DR (%) FPR (%) Precision (%) F-measure
Slowloris 96.23 94.95 2.57 95.14 0.9606
Pwnloris 95.48 98.76 7.62 90.88 0.9589
Httpbog 94.79 97.16 7.36 88.18 0.9368
Mean 95.50 96.96 5.85 91.40 0.9521
Total 94.03 96.65 7.75 91.26 0.9513

The results of the six LDoS attacks in the scene

Sample number Attack type Accuracy (%) DR (%) FPR (%) Precision (%) F-measure
K=6 Slowloris 96.46 95.07 3.82 96.67 0.9727
Slow POST 93.97 96.29 8.43 91.52 0.9432
Slow Read 93.26 92.86 4.19 96.71 0.9514
Pwnloris 99.94 99.53 1.53 98.26 0.9952
Torshammer 91.81 93.19 7.67 92.13 0.9319
Httpbog 65.30 62.11 29.86 78.56 0.7061
Mean 90.12 89.84 9.25 92.31 0.9168
K=12 Slowloris 97.56 95.48 1.72 99.23 0.9815
Slow POST 94.61 93.43 3.28 96.97 0.9552
Slow Read 95.37 95.74 4.66 95.35 0.9633
Pwnloris 98.01 98.86 1.57 98.68 0.9969
Torshammer 94.88 93.74 4.29 95.39 0.9566
Httpbog 68.96 66.93 28.45 74.02 0.7118
Mean 91.57 90.7 7.33 93.27 0.9276

Test results of six LDoS attacks

Attack type Accuracy (%) DR (%) FPR (%) Precision (%) F-measure
Slowloris 98.77 99.02 2.17 97.01 0.9897
Slow POST 99.83 98.91 2.14 95.99 0.9859
Slow Read 97.82 98.95 2.19 96.61 0.9794
Pwnloris 98.05 98.68 1.88 96.07 0.9792
Torshammer 98.09 99.06 2.04 95.97 0.9676
Httpbog 97.05 99.02 2.09 97.97 0.9741
Mean 98.27 98.94 2.085 96.60 0.9793
Total 96.85 96.09 3.12 98.71 0.9738
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