Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks

computer engineering

Software Defined Networking (SDN) is a promising technology for the future Internet. However, the Intrusion detection paradigm introduces new attack vectors that do not exist in the conventional distributed networks. This paper develops a hybrid Intrusion Detection System (IDS) by combining the Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). The proposed model is capable of capturing the spatial and temporal features of the network traffic. Two regularization techniques i.e., L2 Regularization (L2Reg.) and dropout method are used to overcome with the overfitting problem. The proposed method improves the intrusion detection performance of zero-day attacks. The In KDD cup dataset — the most recent dataset for NSL-KDD networks is used to test and evaluate the performance of the proposed model. The results indicate that integrating the CNN with LSTM improves the intrusion detection performance and achieves an accuracy of 96.32%. The estimated accuracy is higher than the accuracy of each individual model. In addition, it is established that the regularization techniques improve the performance of the CNN algorithms in detecting new intrusions when compared to the standard CNN+LSTM. The findings of this study facilitate the development of robust IDS systems for SDN environment.

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