Convolutional neural network-based high-precision and speed detection system on CIDDS-001

Abstract

The growing interconnection of complex infrastructures gives very advanced communication functionalities, which gives a massive increase in connected devices and an associated flow volume. Cloud computing is constantly threatened by sophisticated attacks, which poses challenges for a security system. The Cloud will obsolete existing detection procedures against cyber-attacks where they would not be adapted accordingly. Intrusion detection is a classification problem wherein various machine learning and data mining techniques are applied to classify the network data into normal and attack traffic. Therefore, the proposal of new rapid and effective detection approaches is an absolute necessity. In this work, a proposed framework is a network anomaly detection system based on Deep Learning. The analysis carried out was based on the hyper-parameters of the layers of our model. This proposed model is a combination of two techniques; namely, a reduction of dimensions based on the approach of the main components and the second is based on a dense supervised neural network based on convolution neural network (CNN) for a multi-classification of normal and intrusive events from a recent data-set Coburg Network Intrusion Detection data-set (CIDDS-001). The experiments carried out show that the very precise choice of hyper-parameters gives better results. By running the proposed CNN model, it is capable of detecting attacks with an accuracy of 99.13% and an execution time of 12 s.