A Prediction of Network Intrusion Using CNN-LSTM

At present, network attacks have become a worldwide issue as they disturb the functioning and performance of the computer network. Network attacks are a serious problem they may cause the loss of important information, hacked personal data, and threats for demands money. Intruders today use more advanced type methods for hacking personal data and information as they break the traditional techniques used in the prevention of network intrusion. A more powerful and successful method is required for prediction. The deep learning method is suitable for this problem which is powerful and efficient in prediction. One of the famous types of deep learning models is the convolutional neural network model and another Long Short Term Memory. In this paper, the convolution neural network model combined with LSTM is proposed for the prediction of network intrusion which is one-dimensional. The proposed model is multiclass and tuned by different parameters to obtain the best efficiency from the model in the case of the multiclass dataset. This multiclass model is trained on the two multiclass datasets to get the best accuracy from the model on datasets. The first dataset named as wireless network dataset which contains four or five types of intrusion. The second dataset is the Microsoft Malware dataset that contains the eight or nine-class intrusion type. The experiment from the proposed model gives  0.996%, and 0.985% accuracy performance in multiclass prediction of network intrusion. The performance of the proposed hybrid CNN-LSTM model shows better performance than existing approaches.