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Network anomaly detection and classification using a combination of two artificial neural networks

DOI 10.18127/j19998554-201902-05


O. I. Shelukhin – Dr.Sc. (Eng.), Professor, Head of Department of Information Security, Moscow Technical University of Communication and Informatics
V. O. Musatov – Master’s Degree Student, Department of Information Security, Moscow Technical University of Communication and Informatics

We consider the detection of network attacks using two different artificial neural networks (ANNs): “fast” one and “slow” one. At the first stage, in order to increase the reliability of network anomaly detection, it has been proposed to submit the analyzed traffic to the “fast” primary ANN, recording the presence of any deviations from the norm. At the second stage, a “slow” ANN is used, which improves the classification accuracy of anomalous traffic.
A two-layer SVM (Support Vector Machine) type ANN {36-48-24-1} is used as “fast” one in the work. The main requirement for this ANN is high reliability of anomaly fixation, which is achieved by minimizing of type II errors with an acceptable level of type I errors. A three-layer ANN {36-36-48-36-6} based on BFGS (Broyden – Fletcher – Goldfarb – Shanno) algorithm is used as a “slow” ANN.
The proposed ANNs have been synthesized as follows. At the first stage, the ANN type which is implemented using the scikit-learn library has been selected. At the second stage, the structure of the ANN has been specified, including the number of layers and neurons in each layer. At the third stage, the obtained INS has been trained on the NSL KDD training set, until it reaches the limit of iterations or is stopped using the early stopping method.
For the experimental evaluation of the results obtained, the KDD CUP 1999 dataset has been used. It has been shown that in the considered classes of normal and anomalous traffic containing network attacks (DoS, R2L, U2R, Probe), the proposed hybrid structure demonstrates higher detection accuracy than conventional machine learning methods (SVM and Naive Bayes) or single Back Propagation Neural Network {36-36-36-6}. The model under study has been synthesized using the scikit-learn machine learning library for the Python programming language.
The main advantage of the proposed structure is the ability to detect attacks with a low frequency of occurrence of such records in the sample and a large number of classification features.

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