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Tsinghua Science and Technology  2021, Vol. 26 Issue (4): 484-495    doi: 10.26599/TST.2020.9010022
Intrusion Detection System Using Voting-Based Neural Network
Mohammad Hashem Haghighat*(),Jun Li*()
Department of Automation, Tsinghua University, Beijing 100084, China.
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Several security solutions have been proposed to detect network abnormal behavior. However, successful attacks is still a big concern in computer society. Lots of security breaches, like Distributed Denial of Service (DDoS), botnets, spam, phishing, and so on, are reported every day, while the number of attacks are still increasing. In this paper, a novel voting-based deep learning framework, called VNN, is proposed to take the advantage of any kinds of deep learning structures. Considering several models created by different aspects of data and various deep learning structures, VNN provides the ability to aggregate the best models in order to create more accurate and robust results. Therefore, VNN helps the security specialists to detect more complicated attacks. Experimental results over KDDCUP’99 and CTU-13, as two well known and more widely employed datasets in computer network area, revealed the voting procedure was highly effective to increase the system performance, where the false alarms were reduced up to 75% in comparison with the original deep learning models, including Deep Neural Network (DNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU).

Key wordsdeep learning      Voting-based Neural Network (VNN)      network security      Pearson correlation coefficient     
Received: 24 March 2020      Published: 12 January 2021
Fund:  National Natural Science Foundation of China(61872212);National Key Research and Development Program of China(2016YFB1000102)
Corresponding Authors: Mohammad Hashem Haghighat,Jun Li     E-mail:;junl@
About author: Mohammad Hashem Haghighat received the BS degree in computer engineering from Shiraz Azad University, Shiraz, Iran in 2008, and the MS degree in computer engineering from Sharif University of Technology, Tehran, Iran in 2010. He is currently a PhD candidate at Tsinghua University, Beijing, China. His research interests include network security, intrusion detection systems, deep learning, and information forensics.|Jun Li received the PhD degree from New Jersey Institute of Technology (NJIT) in 1997, and the MEng and BEng degrees in automation from Tsinghua University in 1998 and 1985, respectively. He is currently a professor at the Department of Automation, Tsinghua University, and his research interests include network security and network automation.
Cite this article:

Mohammad Hashem Haghighat,Jun Li. Intrusion Detection System Using Voting-Based Neural Network. Tsinghua Science and Technology, 2021, 26(4): 484-495.

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Fig. 1 RNN architecture[15].
Fig. 2 SAE architecture.
VNNVoting-based Neural Network
DDoSDistributed Denial of Service
ANNArtificial Neural Network
DNNDeep Neural Network
CNNConvolutional Neural Network
RNNRecurrent Neural Network
LSTMLong Short-Term Memory
GRUGated Recurrent Unit
BMBoltzmann Machine
SAEStacked Auto-Encoder
SVMSupport Vector Machine
P2PPoint to Point
Table 1 Acronyms used through the paper.
Fig. 3 VNN architecture.
Fig. 4 Evolution of NSLKDD dataset.
Hyper parameterValue
Train size90%
Test size10%
Batch inputOn
Activation functionRelu
Layers number of CNN4
Layers number of LSTM2
Layers number of CNN-LSTM4
Layers number of DNN2
Layers number of GRU2
Number of input attributes37
Number of input subsets38
OutputBinary and five-class
Table 2 Hyper parameters used to test KDDCUP’99.
Fig. 5 Normalized form of model accuracy (The blue dashed lines show UMT).
MethodNumber of errorsNumber of correctionsCorrection rate (%)
Five-classDNN205 43925 49712.41
CNN205 30674633.64
LSTM208 84981 26338.90
CNN-LSTM85 06863 67574.85
GRU208 51328 37413.61
Table 3 KDDCUP’99 error correction.
Fig. 6 System accuracy: voting-based vs. normal-based using KDDCUP’99 dataset.
ActualNormal301 031203301 234
Malicious488188 121188 609
Total301 519188 324489 843
Table 4 KDDCUP’99 binary classification confusion matrix.
ActualNormal277 26921920 60800298 096
DoS490188 107570188 609
Total277 759188 40323 67470489 843
Table 5 KDDCUP’99 five-class classification confusion matrix.
Binary classification0.00110.00160.99860.99930.99840.9989
Five-class classification0.09820.00210.95630.93020.99790.9628
Table 6 Measurement result of KDDCUP’99 study.
Fig. 7 VNN vs. other deep learning architectures.
DayNumber of flows (million)Botnet (%)Normal (%)Command and control (%)Background (%)
Table 7 CTU13 label distribution.
Fig. 8 SAWANT window-based feature extraction procedure.
Fig. 9 SAWANT architecture.
Hyper parameterValue
Train size10%
Test size90%
Batch inputOn
Activation functionRelu
Number of CNN layers4
Number of LSTM layers2
Number of DNN layers2
Number of GRU layers2
Number of input attributes72
Number of input subsets73
OutputMalicious rate
Table 8 Hyper parameters used to test CTU-13.
Fig. 10 Model accuracy reported by the system during the training phase.
MethodNumber of errorsNumber of correctionsCorrection rate (%)
DNN17 11212 41872.57
CNN76 523825110.78
LSTM668 597272 50740.74
GRU630 54190 90214.42
Table 9 CTU-13 error correction.
Fig. 11 System accuracy: voting-based vs. normal-based using CTU-13 dataset.
ActualNormal2 103 0582542 103 312
Malicious767145 921146 688
Total2 103 825146 1752 250 000
Table 10 CTU-13 confusion matrix.
Table 11 Measurement result of CTU-13 study.
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